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ÖgeA novel artificial intelligence based energy management system for microgrids(Graduate School, 2023-06-19) Aksoy, Necati ; Genç, V. M. İstemihan ; 504182007 ; Electrical EngineeringIn many countries, including our own, large amounts of electrical power are generated where the energy source is located, while it is consumed in areas with large industries and populations. This distance between energy generation and consumption leads to the transmission of energy, which results in the waste of energy as heat and increases energy costs. Microgrids have emerged as a solution to energy use by applying the principle of energy generation and consumption at the same place. Microgrids are small-scale electrical grids that can use distributed energy resources in conjunction with conventional grids. They can combine solar panels or plants, wind turbines, energy storage systems, generators, and the utility grid. This reduces energy loss during transmission, improves energy efficiency, and allows energy to be used efficiently. In addition, microgrids that operate in small settlements such as university campuses, military facilities, towns, or neighborhoods can work in "island mode" without a connection to the utility grid when needed. Many microgrids are currently operated using classical control methods and operate in certain size that has only been determined using optimization methods. This limits the efficiency that can be achieved during the operation of the microgrid and makes it difficult to follow new trends in energy storage technologies. The crux and significance of this thesis revolves around the notion that contemporary energy storage technologies can be utilized efficiently within the system, and that the existing artificial intelligence technology can serve as the foundation of the microgrid energy management system. The energy management system designed in this structure reduces energy waste, lowers costs, improves efficiency, and improves grid stability, while also producing effective solutions for energy demand by controlling the use of various sources together. Moreover, this energy management system contributes to reducing carbon emissions while allowing for the easy adaptation of new technologies. In light of all these advantages, this thesis presents an artificial intelligence-based energy management system design for microgrids. To further explain the concept of artificial intelligence, it encompasses machine learning algorithms as a subset, while machine learning includes deep learning algorithms and concepts. In this thesis, microgrid applications of various sizes and properties are examined, and a microgrid simulation model was created at commonly used sizes. This simulation model assumed a microgrid applied to a university campus, with a solar power plant and wind turbines serving as renewable energy sources. The energy management system being designed predicts the power that these sources will generate, using the up-to-date prediction algorithms within artificial intelligence. When designing, the focus is initially on predicting the power that solar and wind turbines will generate, using five years of meteorological data collected at five-minute intervals. The meteorological dataset, consisting of nine different data types, has undergone a series of data pre-processing. Missing data is filled in accordance with the characteristics of the dataset, and outliers are removed. The characteristics of this dataset were analyzed with different graphs and their suitability for training was examined. The labeled data consisting of the generation values at the same region and at the same time/minute intervals were added to the meteorological data set that was deemed suitable for training. Seven prediction models were developed using four prevalent machine learning methods and three novel algorithms based on the gradient boosting machine to predict the power generated by the solar power plant. These prediction models were trained separately using the training dataset made suitable for training. The results obtained from these seven prediction models were presented in both graphical and tabular formats. In addition to comparing which algorithm gave how successful results for this study, the computation costs were also compared. The designed energy management system must also predict the power generated from wind turbines. In this regard, prediction models were created using three different machine learning algorithms, and the results were obtained. These prediction models were compared using various performance metrics. This study conducted within this thesis, which achieved successful results, offers new approaches and unique results to the literature on the prediction of the power generation of renewable energy sources. An artificial intelligence-based energy management system should provide not only energy efficiency but also low energy costs and profitability for the user. The widespread use of dynamic electricity pricing should also be considered, which is determined based on the relationship between countrywide generation and consumption level. In this thesis, it is assumed that the microgrid simulation model developed is located in a country where dynamic pricing is applied. A five-year dataset was created from actual dynamic pricing data obtained from open-source platforms and analyzed. The dataset was examined, preprocessed, and made ready for the training of prediction models. Four deep learning algorithms with memory cell structures were selected for this study. Using these algorithms and the training dataset, price prediction models were developed, and the results were obtained. The learning performances, error values, and accuracies of the models were presented comparatively. These innovative prediction models were integrated into the designed energy management system. Knowing the power demand from a microgrid makes operational decisions more appropriate and robust. The load demand at which time of the day is an important parameter. Knowing the load demand in advance affects decisions regarding resource utilization. Considering this fact, the energy management system designed should also be able to predict load demand. To this end, load demand prediction models were developed using four deep learning methods with memory cell structures similar to price prediction. Actual load values obtained from open sources were scaled according to the simulation model of the microgrid created. Deep learning models were trained using the five-year load dataset, and the results were obtained. The results were presented comparatively using many performance metrics. As a result of this study, successful prediction models were developed and integrated into the designed energy management system. An artificial intelligence-based energy management system uses many prediction models described above. The theoretical and mathematical foundations of all machine learning and deep learning methods used are provided in the second chapter of this thesis. The energy management system described requires an additional controller to manage the microgrid in addition to human management. In this context, this thesis proposes another artificial intelligence-based controller. Data-driven control methods that have replaced classical control methods are popular topics nowadays. This thesis focuses on machine learning-based control methods of this type. In this context, reinforcement learning, which is one of the three main branches of machine learning, is investigated and its foundations are given. Reinforcement learning is the general name for methods based on the principle of controlling the system without the need for a mathematical model of the system. It is possible to separate this concept into methods based on table creation and methods using deep neural networks. In this thesis, controller agents using both types of methods are created. The agent, which will learn to control the system in reinforcement learning, needs to optimize itself. This optimization process is done through trial and error. For the agent to be able to take the best version through these trials, the system it will control, which is a microgrid environment model in this thesis, must have specific characteristics. Five different control agents were designed specifically for the energy management system, three of which were temporal-difference-based and two were deep reinforcement learning-based. Three environment models designed specifically for the microgrid are proposed in this thesis to enable these agents to train themselves. These environment models with unique reward strategies present a new approach to the literature. These environment models that use renewable energy sources, load demand, and dynamic prices for the training of agents have shown quite successful results in terms of energy management. The trained reinforcement learning agents have learned to manage the microgrid and offer considerable profitability to the user. The energy management system whose design steps are explained in this thesis uses many different artificial intelligence algorithms. These artificial intelligence models created, trained, and successful results achieved have been consolidated under a single graphical interface in this thesis. A unique graphical interface has been designed, and all prediction models and control agents have been integrated into this design. This interface design, which consists of seven pages in total, offers many variables and control actions related to the microgrid to the user. The user can see the powers that will be generated for the future, load demand, and the price. In addition, the user can apply many control actions to the microgrid through this interface. The user, who can also see many real-time parameters, can analyze the performance of prediction models and control agents through relevant pages. In conclusion, this thesis proposes an artificial intelligence-based energy management system that contains many current and innovative algorithms for microgrids and uses them uniquely. Artificial intelligence-based prediction models determine the decisions that an artificial intelligence-based control agent will make. This agent, which learns to select the correct control actions for the microgrid, presents the determined control action to the user through the designed interface. Additionally, the originally designed energy management system interface allows the user to see many parameters related to the microgrid in advance. This thesis proposes an energy management system that contributes to the literature with its original approach and can be used in real-world applications.
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ÖgeA peak current controlled dimmable sepic led driver with low flicker(Graduate School, 2022-01-18) Örüklü, Kerim ; Yıldırım, Deniz ; 504181056 ; Electrical Engineering ; Elektrik MühendisliğiNowadays, a considerable part of the energy consumption in the world has been formed by lighting sources used in buildings, industry, transportation, and commercial. Yet, there has been a rapid decrease in traditional energy resources. Therefore, an energy efficient lighting system could be a solution to global energy problem. Light-emitting diodes (LEDs) have been taken much attention lately and expected to replace with classical lamps due to their special characteristics like high efficiency, long lifetime, environment friendly, robustness, and small size. However, a driver circuit is required to operate LEDs and constant current drivers can improve the LEDs performance. Hence, studies on LED driver circuits and its control method have recently been increased both in industry and in academia. In some applications, it is desirable to have control on LED brightness. This can be done by a current-control method that adjust the current flowing through LEDs. But, there are recommended practices while modulating current in High-Brightness LEDs for mitigating health risk to viewers in IEEE Std. 1789-2015. Most of the driver circuit have put on the market without any flicker measurements and checking these recommended practices about percent flicker and flicker index. All light sources may have flicker with various levels. However, the flicker generally exists in LED lighting when AC to DC conversion is present. Because of the full-wave bridge rectification in AC-DC LED drivers, LED lamps will have a peak-to-peak current ripple at twice the line frequency (100 Hz or 120 Hz). Hence, the flicker is mainly dependent on the driver circuit for LED lighting. Health risks and biological effects of flicker to the viewers such as headache, eyestrain, and seizures cannot be ignored and should be taken into consideration when designing a LED driver. A flicker-free LED driver can improve the visual performance and offer a human health friendly lighting. In this thesis, a peak-current control method is proposed for 30-Watt Single Ended Primary Inductor Converter (SEPIC) LED driver with adjustable output current. The proposed control strategy is based on measuring MOSFET peak current value using a shunt resistor. When this voltage reaches peak threshold value, controller turns off switch. The output current is adjusted to desired levels by changing this peak threshold value. Both simulation and implementation of the driver have been carried out. 220V rms, 50 Hz AC main is used as input of the driver. Pulse Width Modulation (PWM) signals are generated by using UC3842 and TL3845 Integrated-Chips (IC). Flicker measurements are taken from the output current curve. To validate proposed peak current control method, a 33.6 Watt, 112 V / 0.3 A SEPIC LED driver prototype is constructed and tested. Analysis and measurements have been carried out for different output current levels. Peak efficiency is obtained as 88.4% at nominal output current. Furthermore, 5.806% and 6.540% of percent flicker have been obtained at 300mA and 100mA, respectively. It has been found that the proposed Peak-Current-Mode-Controlled SEPIC LED driver offers LED brightness control for the consumer comfort, a high efficient system for energy efficiency, and a low-risk level of flicker for human health.
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ÖgeAdaptive signal processing based intelligent method for fault detection and classification in microgrids(Lisansüstü Eğitim Enstitüsü, 2021) Azizi, Resul ; Şeker, Şahin Serhat ; 724566 ; Elektrik MühendisliğiThe ever-increasing energy demand, the environmental issue of fossil fuels and the high investment cost for the establishment of bulk power plants lead energy plans to more flexible and scattered small-scale energy sources. The main feature of these new topologies is that they consume renewable energy sources for electricity generation. It also requires less time to plan, build and operate. Moreover, they are close to energy sources and local loads. So, there are more efficient, with minimal environmental issues. However, besides their benefits and advantages, they pose a new challenge for traditional power systems. These challenges include protection issues, stability concerns, and complex control systems and so on. Traditional power systems include mass generation followed by transmission and distribution. In this topology, it is possible to plan generation because consumption at the transmission level of the power system is more predictable and fuel resources are always available for generation units. On the other hand, the transmission system and its conditions can be controlled by state estimators and SCADA system. Therefore, production and consumption uncertainties are minimal and conventional protection is sufficient to protect these systems. Also, distribution systems have no generating units, systems are mostly radial and overcurrent protection systems are sufficient to protect them. In these passive networks, it is not necessary to have fast and reliable protection systems as in transmission systems. The initial role of these new energy sources was to act as a backup for mass production and to eliminate the small generation and consumption mismatch during peak consumption. On the other side, huge demand growth and investment time of mass production units and environmental concerns make these distributed energy resources (DERs) (wind, solar, biomass, etc.) popular in the distribution system. However, the contribution of the early DER groups to the total production is low and the control systems are very sensitive to voltage disturbances such as faults. Thus, according to the grid codes, after any minor fault or disturbance in the system, the DERs are disconnected, synchronized manually and reconnected after the fault is cleared. With the increasing penetration of DERs in distribution systems, they play an important and rapidly increasing role in the total production of the system. Therefore, de-energizing all these DERs in an area in the distribution system after a fault has occurred can lead to stability problems due to generation and consumption imbalance. Accordingly, a new concept called microgrid emerged and mainly established in distribution systems. This topology is the microscale of the power system. It can operate autonomously and cover the total demand of this local distribution system. Like the SCADA power system, it has an equivalent centralized monitoring and control system. The total generation is almost sufficient for the total demand of the loads in distribution networks converted to microgrid. It can operate as a standalone ecosystem separated from the main grid and is self-sufficient. The basic requirement of this topology for connecting to the main grid through PCC (point of common coupling) is to increase the total inertia of the system and increase the post-fault stability region. In addition, this topology can transfer energy to the main system if it produces more power than the loads consume. This can reduce the stress of mass production units. Last but not least, if the main upper grid disturbed, the microgrid can continue to supply its loads by disconnecting from the grid. In this new concept, grid codes expect the micro grid to be able to ride through faults and disturbances thanks to low voltage ride through (LVRT) systems. In fact, as a micro-scale model of the power system, the voltage of the DERs at the time of fault occurance is controlled by the LVRT, and the DERs continue to operate without disconnection after the fault is cleared by circuit breakers or other elements). Therefore, more complex control systems are required for DERs. However, microgrids are distribution systems and unlike traditional power systems, there is a high amount of uncertainty in generation and consumption (loads). The distribution system has changed from a passive network to an active dynamic network. In this system, topology, generation and consumption are changed faster and faster than in conventional power systems. This situation constantly changes the fault current level and direction, and the conventional overcurrent protection is completely insufficient to protect them. Also, due to the high penetration of sensitive DERs, prolonged fault current is not allowed (stability concerns). Moreover, inverter-based DERs have a very small contribution to the fault current level. The current protection method of microgrids is adaptive protection. In this model, all operating conditions of the system are extracted and all components of the systems are continuously monitored by central or decentralized control system or even dynamic load estimation. This model cannot be applied to a central control system because it has to process large amounts of data at a high sampling rate and it is impossible to make real-time decisions. Based on these facts, a new intelligence-based method for fault detection and classification of microgrid is proposed in this thesis. In the proposed method, three different adaptive signal processing methods are used to extract the short-time transient component of the signal instead of the fault current level. It transfers data (feature extraction) into three different data spaces. The main feature of these signal processing methods is that they do not use a predefined basis to decompose a signal. The basis is adaptive to signal and extract components depend on the noise penetration level and frequency components of the signal. An intelligence-based method called Brwonboost is used to make decisions in these data spaces, and the total decision is taken by the majority of votes of these three intelligence-based methods in these three data spaces. The main unique feature of the proposed method compared to traditional machine learning methods is its adaptability and uses a non-convex optimization method for detection and classification. The proposed method is a set of weak classifiers and tries to learn the data space step by step and iteratively. It tries to adapt the data by classifying the data that was misclassified in previous iterations. On the other hand, the unique non-convex optimization feature of the proposed method gives it an intelligence to select or discard misclassified data. It can decide step-by-step removal of the algorithm's iteration data in the training process if there is an outlier or a violation in another class area. This feature provides evidence against overfitting and becomes as practical a method as it is for real-world measured data. Finally, a Brownboost decision is also made by a majority vote of the weak classifiers. An intelligence-based method called Brwonboost is used to make decisions in these data spaces, and the total decision is taken by the majority of votes of these three intelligence-based methods in these three data spaces. In this method the classifier works base on the margin. This means, instead of only finding a classifier that minimize the classification error, it selects a classifier that has maximum discrimination between data of every class. The unique feature of the proposed method compared to traditional machine learning methods is its adaptability and uses a non- convex optimization method for detection and classification. The proposed method is an ensemble of weak classifiers and tries to learn the data space step by step and iteratively. It tries to adapt to the data by classifying the data that was misclassified in previous iterations. On the other hand, the unique non-convex optimization feature of the proposed method gives it an intelligence to select or discard misclassified data. During this step-by-step process, the algorithm can detect outliers or misclassified data that intensely violated other class area and remove it. This feature makes it robust against overfitting and becomes as practical method for real-world measured data. In total, the proposed method tries to classify the data in three different data spaces. The data area that makes maximum distinction between the data of each class is less sensitive to noise. Thus, a classifier has are fewer generalization errors to unseen new data (higher margin). Therefore, its Brownboost has more voting power in decision making. The results are test in test benchmark microgrid. DERs are modeled with the detailed model to extract the true detail form of the signal. Various types of control model and fault ride thruogh feature of DERs are implemented.
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ÖgeAn agent-based energy management approach for V2X-capable charger clusters(Graduate School, 2023-01-05) Akyün, Gülen ; Yılmaz, Murat ; 504191071 ; Electrical EngineeringTo deal with the intermittency problem of renewable-based distributed generation, flexible energy assets such as electrical batteries are widely considered. In line with the localization trend in the energy sector, electric mobility is becoming mainstream. The additional load demand that comes with the penetration of EVs will raise the need for additional electricity generation. In particular, aggregated charging load of electric vehicles cause overload in the distribution network. With the management of EV charging, overload can be avoided and grid reliability can be ensured. At this point, smart grid applications promise to help make the addition of electric vehicles to the grid more sustainable with concepts such as V2X (vehicle to everything). On the other hand, as the plug-in EV fleet grows, an effective energy management system is needed to avoid adverse effects such as voltage fluctuations and increased electricity losses. By combining several flexible energy assets, a bidirectional EV charger cluster can have a local balancing capacity and therefore be operated without demanding energy from the grid for a specified period of time. The aim of this thesis is to manage EV charging in clustered systems and to obtain energy neutral charger clusters by increasing the local balancing capabilities of clusters and to efficiently use V2X functions with the proposed energy management algorithm. With this thesis, it is also aimed to reduce the peak-to-average ratio and to provide a balanced and efficient load profile. To achieve the objectives, an agent-based energy management concept has been proposed. In the proposed concept, each bidirectional charging unit with a connected EV at the charging station is represented by an agent. This approach provides a decentralized structure and swarm control in line with the agents' local targets. In this algorithm all power producers and consumers are represented as agents. First, the agents calculate their operation range and current power demand or production, i.e. their flexibility. Energy consumers and producers then interact and negotiate with each other, thus providing self-consumption by meeting each power consumption with an equivalent power generation. This allows flexible power transfer between EVs with a collaborative perspective on the charging system. In this way, the peak-to-average ratio decreases and self-consumption increases. In the study, the negotiation and decision-making processes of the agencies are discussed in detail. Simulation studies performed on the proposed concept for local balancing show that this application has the potential to provide effective and sustainable solutions for energy management.
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ÖgeBatarya şarj uygulamalarında kullanılan LLC rezonans çeviricilerde optimum verim eldesi için yeni bir yöntem(Lisansüstü Eğitim Enstitüsü, 2022-08-15) Çalışkan, Eser ; Üstün, Özgür ; 504112010 ; 504112010Dünya genelindeki nüfus artışı ve globalleşme, mobilite kavramını tetiklemiştir. Mobilite ile yeni teknolojilerin hayatımıza girmesi kaçınılmaz olmuştur. Yeni teknolojilerin hayatımıza girmesi her geçen gün artan enerji talebini beraberinde getirmektedir. Günümüzde ulaşımda enerji talebinin büyük bir kısmı petrol ve petrol türevleri olan yakıtlar tarafından karşılanmakta olup gelecekte alternatif enerjilerin kullanıma alınmasını zorunlu kılmaktadır. Hayatın birçok alanında mobil olma ihtiyacının yanı sıra bunun bir sonucu olarak ortaya çıkan enerji gereksiniminin de mobiliteye hizmet edecek şekilde taşınabilir ve paylaşımlı olması kaçınılmazdır. Mobiliteye en çok hizmet eden cihazların başında elektrikli araçlar gelmekte olup her geçen gün yeni bir model piyasaya sürülmektedir. Elektrikli araçlar ve neredeyse tüm mobil cihazlarda enerji ihtiyacı büyük çoğunlukla dahili bataryalar ile sağlanmakta olup şarj ve deşarj işlemleri ile enerji paylaşımı sağlanabilmektedir. Batarya şarj ve deşarj döngüsünde enerji kayıplarının en az seviyeye indirilebilmesi için kullanılan güç çeviricisi tüm çalışma bölgesinde en yüksek verim ile çalıştırılmalıdır. Güç elektroniği çeviricisinin mümkün olan en yüksek verim ile çalıştırılabilmesi amacıyla farklı kontrol yöntemleri ve devre topolojileri geliştirilmektedir. Bu doktora tez çalışmasında, yeni tip GaN anahtarlama elemanları kullanılan bir LLC rezonans çeviriciye yönelik yeni bir verim optimizasyonu yöntemi üzerinde durulmuştur. Hafif elektrikli araçlar için tüm batarya şarj sürecinde en yüksek verim ile güç akışı kontrolünün en iyileştirilmesi amacıyla yeni bir verim optimizasyonu algoritması geliştirilmiştir. Klasik kontrol yöntemi olan frekans modülasyonu (FM), ölü zaman kontrolüne dayanan S-PWM ve kesintili çalışma modları LLC rezonans çeviricinin verim değerinin tüm batarya şarj sürecinde mümkün olan en yüksek seviyede kalması amacıyla birlikte kullanılmıştır. İlk olarak potansiyel batarya şarj topolojileri incelenmiş olup ardından bir rezonans çevirici kullanılarak klasik bir batarya şarj sürecine ait grafik verilerek şarj bölgeleri ve temel verim problemi ele alınmıştır. Düşük ve yüksek yük durumları arasındaki farklar ve rezonans çeviricinin çalışma karakteristiği birlikte değerlendirilerek özellikle düşük yük durumlarında çevirici veriminin düşmesine ait detaylar aktarılmıştır. Problemin tanımının ardından GaN tipi anahtarlar kullanılan bir LLC rezonans çevirici ile alakalı teorik altyapıya değinilmiş olup yapılan detay tasarımlar, hesaplamalar, elektronik kartlara ait şema ve baskı devre çizimleri, VHDL blokları ve tasarımları, kart testleri ve doğrulaması verilmiştir. LLC rezonans çevirici tasarımlarını takiben üç farklı anahtarlama ve kontrol yöntemine ilişkin modelleme ve benzetim çalışmalarına yer verilmiştir. Benzetim çalışmalarında temel çalışma prensipleri ve modeller, batarya şarj işlemi ve temel dalga şekilleri verilmiştir. Benzetim çalışmalarının ardından yapılan tasarım detaylarına göre üretilen ve entegre edilen deney düzeneği üzerinde üç farklı anahtarlama yöntemine ait testler gerçekleştirilmiştir. Deneysel testlerin sonuçlarına göre iteratif olarak önerilen verim takibi algoritması iyileştirilmiştir. Sonuç olarak önerilen algoritmanın batarya şarj sürecine uygulanması ve oluşturduğu etki tartışılmıştır. Önerilen verim takibi algoritması ile batarya şarj sürecinde kullanılan LLC rezonans çeviricinin toplam verim değerinde özellikle düşük yük durumlarında %25'e varan artış gözlenmiştir. Tez çalışmasında, yeni bir verim takip algoritması ortaya koyularak GaN temelli bir LLC rezonans çevirici üzerinde hafif elektrikli araçlara ait bir batarya şarj uygulamasında testleri ve doğrulaması yapılmıştır. Sonuçlar değerlendirilmiş olup gelecek çalışmalar için bir yol haritası çıkarılmıştır.
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ÖgeCompensation of dead time caused output voltage distortion in SPWM full bridge inverter(Graduate School, 2022-01-18) Polat, Umutcan ; Yıldırım, Deniz ; 504181073 ; Electrical Engineering ; Elektrik MühendisliğiNowadays, inverters have become an indispensable element for many application areas when industrial applications are examined. Inverters are widely used in battery systems, renewable energy systems, control of various electrical machines and power systems. Due to the fact that inverter is often used in industry, studies on inverters have increased recently and inverter technologies are developing gradually. Generally, single-phase or three-phase full bridge voltage source inverters are used in such applications and there are various modulation techniques such as sinusoidal pulse width modulation technique, space vector pulse width modulation technique and etc. to provide voltage and frequency control of these inverters. These various techniques have been developed to minimize switching losses and reduce harmonics in output current and voltage. In real applications, power switches used in power electronics circuits are not ideal. These power switches have turn-on and turn-off time in switching characteristic. Because of this reason, the simultaneous conduction of switches on the same leg causes short circuit in inverter circuit. This situation is undesirable. In order to prevent synchronous conduction of both switches of the same leg at the same time, time delay is inserted to the driving signal of these switches.This time is called as dead time. Although dead time/blanking time has to be used in this circuits as mentioned above, the dead time has a very negative effects in terms of distortion of output waveforms. These problems are distorion of the output voltage and current waveform to contain a significant number of harmonic components at low voltage and high switching frequency. During the dead time, distortion of the voltage and current waveforms can be seen clearly at zero crossings of the current. In literature, this situation is called as zero-current-clamping phenomenon. This effect becomes greater as the switching frequency increases. In order to eliminate or reduce these effects, several approaches have been proposed. These methods can be listed as dead time compensation methods, dead time elimination methods, dead time minimization methods. It is seen that it is necessary to use dead time compensation methods since it is desired that the output voltage of the inverters is close to the sinus form and thus the total harmonic distortion is be reduced to a minimum. In order to provide this, these compensation methods are gradually developed. In this thesis context, time compensation method, which is one of the dead time compensation methods, is used. The turn-on or turn-off time of the power devices are adjusted by changing pulse-width in this method. Pulse-width is increased or decreased at zero crossings of the current. Thus, THD value of output waveforms is decreased by using this method. In this thesis, both simulation and implementation of a voltage source single-phase inverter have been carried out and the sinusoidal pulse width modulation method (SPWM) is used as modulation technique. Digital sinusoidal pulse width modulation is programmed with the help of STM32F407VG microcontroller of STM series. In addition, STM32CubeIDE is used as development tool. SPWM is produced by comparing the sine tables, which is produced by the microcontroller, with the microcontroller counter. This circuit is designed as open-loop system and the modulation index is initially set to a certain value both R and RL loads. While the input voltage of the designed circuit is 400 V, the output voltage is 220Vrms and the switching frequency is 20 kHz. The output power of the designed circuit is between 450 and 480 W at both R and RL loads. In addition, the dead time is 1 µs in all cases. In fixed dead time, output voltage and current for compensated and uncompensated states are obtained by simulation and implementation at R and RL loads. Due to the effect of dead time, harmonic distortions are observed on the output voltage and output current in uncompensated state. In order to minimize this effect, the time compensation method, which is one of the dead time compensation methods, is used within the scope of this thesis as mentioned above. Thus, the harmonic distortion is aimed to be reduced. According to simulation results, while the total harmonic distortion of output voltage is 5.34 at uncompensated state, total harmonic distortion of output voltage is 3.15 at compensated state at R load. On the other hand, while the total harmonic distortion of output voltage is 5.42 at uncompensated state, total harmonic distortion of output voltage is 3.71 at compensated state at RL load. According to experimental results, while the total harmonic distortion of output voltage is 5.89 at uncompensated state, total harmonic distortion of output voltage is 3.86 at compensated state at R load. On the other hand, while the total harmonic distortion of output voltage is 6.02 at uncompensated state, total harmonic distortion of output voltage is 4.50 at compensated state at RL load. According to the results, It has been clearly seen that the applied time compensation method reduces the harmonic distortions on the output voltage caused by the dead time.
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ÖgeDağıtık üretim sistemlerinin akıllı şebekeler üzerine etkilerinin incelenmesi(Lisansüstü Eğitim Enstitüsü, 2022) Pürlü, Mikail ; Türkay, Belgin ; 726855 ; Elektrik MühendisliğiTeknolojinin gelişmesiyle birlikte, elektrik enerjisine olan ihtiyaç ve talep her geçen gün artmaktadır. Artan tüketici talebi karşısında, üretim, iletim ve dağıtım sistemlerinin yetersiz kaldığı durumlar ortaya çıkabilmektedir. Yetersiz kalan bu şebekelerde artan hat yüklenmeleri nedeniyle, kayıp güç artışları, gerilim düşümü problemleri, elektrik kesintisi ve güvenirlik gibi çeşitli önemli problemler ortaya çıkmaktadır ve tüketiciler hem sosyal hem de ekonomik yönden rahatsız olmaktadır. Ayrıca, artan fosil yakıt fiyatları ve azalan fosil yakıt rezervleri nedeniyle yeni üretim teknolojilerine ihtiyaç giderek artmaktadır. Artan hava kirliliği ve iklim bozulmaları gibi çevresel kaygılar, yenilenebilir enerji üretim teknolojilerinde büyük gelişmelere öncü olmuştur. Bu gelişmeler, yenilenebilir enerji sistemlerinden enerji üretim maliyetlerini giderek düşürmektedir. Artan enerji talebini karşılamakta zorlanan şebekelerde, kayıpları azaltmak ve gerilim profilini iyileştirmek amacıyla, ağın yeniden yapılandırılması, kapasitör tahsisi veya dağıtık üretim sistemlerinin tahsisi önerilmektedir. Yeni hatların oluşturacağı ek maliyetler ve fiziksel olarak her zaman uygulanabilir olmaması gibi nedenler ağ yeniden yapılandırmasını zorlaştırmaktadır ve dağıtık üretim ön plana çıkmaktadır. Kayıpları azaltmak, gerilim profilini iyileştirmek, şebekeye bağlı kesintilere çözüm üretmek ve çeşitli güç kalitesi katkıları nedeniyle merkezi üretim yerine, ucuz ve sınırsız olan yenilenebilir enerji kaynaklarını da üretime kazandırabilen, dağıtık üretim teknolojilerinin kullanımı giderek yaygınlaşmaktadır. Merkezi üretimle beslenen şebekelerde tek yönlü olan yük akışı, dağıtık üretim sistemlerinin entegre edilmesiyle birlikte çift yönlü olarak gerçekleşmektedir. Bu durum güç kayıplarında artışa ve koruma sistemlerinde arıza algılama sorunları gibi çeşitli olumsuz sonuçlara yol açabilir. Ancak, dağıtık üretim sistemleri tahsis edilmeden önce çeşitli analizler yapılarak planlanırsa, kayıpları ve gerilim devinimini azatlma, gerilim profilini ve gerilim kararlılık indeksini geliştirme, güvenirliği artırma ve şebekeye bağımlılığı azaltma gibi pek çok katkıyı beraberinde getirmektedir. Literatürde, dağıtık üretim sistemlerinin optimum tahsisini gerçekleştirmek için analitik yöntemler, sezgisel yöntemler ve hibrit yöntemler önerilerek, çeşitli IEEE test sistemleri veya ülkelerin gerçek dağıtım şebekeleri üzerinde test edilmiştir. Bu çalışmada, güç kayıplarını azaltmak ve gerilim profilini iyileştirmek amacıyla dağıtık üretim sistemlerinin optimum tahsisi gerçekleştirilmiştir. Bu amaçla, sezgisel algoritmalardan olan, Genetik Algoritma ve Parçacık Sürü Optimizayonu algoritmaları önerilmiş ve IEEE 33 baralı radyal dağıtım sistemi üzerinde uygulanmıştır. Öncelikle, literatür kıyaslaması yapabilmek ve algoritmaların doğruluğunu kanıtlamak amacıyla, puant yük talebi için dağıtık üretim tahsisi gerçekleştirilmiştir. Tüm dağıtık üretim tipleri ve özellikle literatürde kullanılmayan Tip IV için optimum tahsis, üç farklı senaryo özelinde gerçekleştirilmiştir. Analizlere göre en düşük fayda, reaktif güç tüketimi nedeniyle Tip IV ile ve en yüksek fayda hem aktif hem de reaktif güç üreten Tip III ile sağlanmıştır. Parçacık Sürü Optimizasyonu, Genetik Algoritma'ya nazaran daha iyi sonuçlar verirken, her ikisi de minimum kayıp, maksimum gerilim iyileşmesi ve yakınsama gibi açılardan literatürden çok daha iyi sonuçlar vererek, üstünlüklerini kanıtlamışlardır. Algoritmaların güvenirliği ve doğruluğu kanıtlandıktan sonra, asıl hedef olan yıllık toplam enerji kayıplarını ve gerilim devinimi azatlamak amacıyla yenilenebilir enerji kaynaklarının şebekeye optimum tahsisi geçekleştirilmiştir. Mevsimsel üretim ve tüketim belirsizliklerini içeren bu çalışmada, yenilenebilir enerji kaynakları olarak güneş panelleri ve rüzgar türbinleri kullanılmıştır. Yenilenebilir kaynakların sağladığı katkıyı ölçmek ve uygulanabilirliğini kıyaslamak amacıyla, fosil yakıt tüketimine dayalı konvansiyonel kaynaklar da kullanılmıştır. Yapılan çalışmalarda teknik olarak en iyi sonuçlar konvansiyonel kaynaklarla elde edilirken, en düşük katkı ise mevsimsel ve günlük olarak güneş ışınım dağılımının düzgün olmaması sebebiyle, güneş panelleri tarafından sağlanmıştır. Hem güneş ışınım dağılımına nispeten daha düzgün rüzgar dağılımı olmasından dolayı konvansiyonel kaynaklara yakın miktarda teknik katkı sağlayan hem de zararlı sera gazı salınımı olmaması nedeniyle çevreci olan rüzgar türbinlerinin optimum güç faktöründe işletilmesi en uygun dağıtık üretim çözümü olarak önerilmiştir. Literatürde ve yapılan bu çalışmada, dağıtık üretim kaynaklarının tahisinin yük akışı analizlerine dayanması nedeniyle çok fazla zaman aldığı görülmüştür ve bu problemin üstesinden gelmek amacıyla da makine öğrenmesine dayalı bir tahmin metodolojisi önerilmiştir. Makine Öğrenmesi algoritmalarından olan Lineer Regresyon, Yapay Sinir Ağları, Destek Vektör Regresyonu, K En Yakın Komşu ve Karar Ağacı algoritmaları kullanılarak, optimum dağıtık üretim gücünün ve şebekeye etkilerinin tahmini sağlanmıştır. Algoritmaları ve önerilen metodolojinin uygulanabilirliğini göstermek için IEEE 12, 33 ve 69 baralı standart test sistemlerinin gerekli verileri toplanmıştır. Toplanan verilerin %75'i, WEKA programında bulunan makine öğrenmesi algoritmaları ile tahmin modellerinin eğitimi için kullanılmıştır ve %25'lik test verisiyle de algoritmaların performansı ve doğruluğu değerlendirilmiştir. Değerlendirme metrikleri olarak, R-kare (R2) analizi ve ortalama mutlak yüzde hata (Mean Absolute Percentage Error, MAPE) hesaplaması kullanılmıştır. Tüm algoritmalar, kabul edilebilir hata aralığının dışına çıkmayan ve uygulanabilir doğrulukta tahminler gerçekleştirmiştir. Tek giriş değişkeni olan tahmin modellerinde Destek Vektör Regresyonu algoritması ve çok giriş değişkeni olan tahmin modellerinde K En Yakın Komşu algoritması daha başarılı olmuştur. Giriş ve çıkış değişkenleri arasında doğrusal bağlantı bulunmayan verilerin tahmininde ise Lineer Regresyon kabul edilebilir bir sonuç vermemiştir ve kullanımı uygun bulunmamıştır. Dağıtık üretim sistemlerinin optimum boyutunun, yerinin ve güç faktörlerinin belirlenmesinde önerilen sezgisel algoritmalar üstün performans göstermiştir ve yeni bir metodoloji olarak sunulan, dağıtık üretim sistemi optimum boyutu ve şebekeye etkilerinin tahmininde Makine Öğrenmesi kullanımı uygun ve etkin bulunmuştur. Daha büyük sistemler üzerinde çalışılması, enerji depolama sistemlerinin eklenmesi, yeni sezgisel veya hibrit algoritmalarla çözümler, makine öğrenmesi ile birlikte güçlendirilmiş tahmine dayalı çözümler ve farklı yenilenebilir teknolojilerin kullanımı gelecek çalışması olarak önerilmektedir.
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ÖgeDağıtım şebekelerinde aşırı akım rölesi ile adaptif koruma(Fen Bilimleri Enstitüsü, 2020-07) Var, Hakan ; Türkay, Belgin ; 637246 ; Elektrik Mühendisliği Anabilim DalıÇevreyi koruma bilincinin artması, yenilenebilir enerji kaynaklarına ulaşımın kolaylaşması, elektrik fiyatlarının artması bireyleri ve şirketleri kendi enerjisini üretmek istemesidir. Tüm bu etkenler mikro şebekelerin kurulumunu ortaya çıkarmıştır. Mikro şebekeler bölgesel olarak içerdikleri enerji kaynakları ile bölgedeki yükleri beslerler. Bu enerji kaynakları biyogaz, mikro hidroelektrik, güneş santrali, rüzgâr türbinleri, gaz türbinleri, batarya teknolojisi, jeotermal enerji gibi birimlerden oluşmaktadır. Mikro şebeke teknolojisi, mikro şebeke otomasyonu sayesinde ana şebekeye bağlı veya temel şebekeden bağımsız olarak çalışabilmektedirler. Mikro şebekeler arz/talep dengesi gözetiminde temel şebekeye enerji verebilir ya da ana şebekeden enerji alabilmektedir. Ayrıca bir arıza durumunda temel şebeke bağlantısı kesilerek frekans ve gerilim değişimlerinin önüne geçilebilmektedir. Mikro şebekeler hava koşullarından etkilenen rüzgâr türbinleri, güneş santralleri gibi yenilenebilir enerji kaynaklarına sahip olduğundan ve hem temel şebekeye bağlı hem de güç adası biçiminde çalıştırdıklarından dolayı değişken güç akışına sahiptir. Bu durum mikro şebekelerin kontrolü ve korumasını zorlaştırmaktadır. Geleneksel aşırı akım röleleri birçok sistemi başarıyla koruyabilmektedir. Akım genliği arızanın tespiti için akım yön bilgisi ise arızanın koruma bölgesinde olup olmadığını tespit etmek için kullanılır. Sistemdeki ardışık rölelerin koordinasyonu için röleler arasında zaman aralıkları olmalıdır. Bu zaman aralıkları röle ayar parametrelerinden röle ayarında izin verilen maksimum akım (Is) ve zaman ayar çarpanı (TMS) değerlerine bağlıdır. Bu röleler merkezle ve birbirleri ile haberleşmedikleri için Is ve TMS değerlerini tekrar değiştirmek için manuel bir işlem yapmak gerekir. Özellikle mikro şebekelerin bulunduğu sistemlerde görülen şebeke yük akışının değerinin ve yönünün değiştiği durumda geleneksel rölelerin kullanımı güçleşmektedir. Geleneksel aşırı akım rölelerinin aksine, adaptif aşırı akım röleleri şebekedeki akım bilgileri ve kesici durumlarını kontrol eder. Herhangi bir değişiklik saptanmış ise röle parametreleri olan Is ve TMS değerlerini tekrar hesaplayarak yeni değerlere göre devreye girer. Bu durum yük akışının sıklıkla değiştiği sistemlerde adaptif aşırı akım rölelerinin kullanımının avantajını ortaya çıkarmaktadır. Adaptif aşırı akım rölesinin parametrelerinin değişime bağlı olarak ayarlanabilmesi için rölelerin bir ana merkezle haberleşmesi gerekmektedir. Yeni güç akış değerlerine entegre olması için bu haberleşmenin hızlı ve güvenilir olması gerekmektedir. Haberleşme hattının da olduğu bir otomasyon sistemi kurulduktan sonra adaptif aşırı akım rölesiyle sistemi koruma daha güvenilir, hızlı ve kolay olmaktadır. Geleneksel yöntem ile koruma ve adaptif koruma arasında karşılaştırma yapabilmek için örnek bir mikro şebeke sistemi modellenmiş ve her iki koruma modeli bu şebeke sistemine uygulanmıştır. Modellenen mikro şebeke hem ana şebekeye bağlı hem de ada durumunda çalışabilmektedir.
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ÖgeData-driven prediction and emergency control of transient stability in power systems towards a risk-based optimal power flow operation(Graduate School, 2022-09-30) Jafarzadeh, Sevda ; Genç, V. M. İstemihan ; 504172009 ; Electrical EngineeringCost-efficient and reliable operation of power systems is one of the main concerns of the utilities. The large disturbances and major blackouts occurred in last two decades such as the blackout that took place on 14 August 2003 in the Midwest and Northeast US have ruinous and costly effect for millions of customers. The development of a proper stability prediction and control scheme for an emergency condition is the main objective of this study. In this study, a novel framework using two different approaches is proposed and investigated for real-time transient stability prediction (TSP) in power systems where the signals obtained from PMUs are utilized. The first proposed method is based on signal processing and machine learning approaches which take the computed energy of PMU signals in a window of measurements as an input to a classifier to predict the stability of the system. Several types of classifiers, which are multi-layered perceptrons (MLPs), decision trees (DT), and Naïve Bayes (NB) classifiers, are employed. Two alternative approaches of choosing the window of measurements used for TSP are developed, where an MLP-based fault detection process is also proposed to form the proper window of measurements. One approach is to use a fixed window of only post-fault measurements, whereas the other approach is to use an expanding window of measurements covering pre-fault, fault-on and post-fault stages. Utilization of the energy concept in TSP gives the flexibility to process signals in different sizes while providing predictions that are robust to measurement noises and missing data. It also makes feature selection methods directly applicable, making the TSP possible with fewer PMUs. The proposed methods are applied to two different test systems and a large-scale model of the Turkish power system. In the second approach, a novel methodology based on Koopman mode analysis is proposed to predict the transient stability of a power system in real-time. The method assesses the stability of the system based on a sliding sampling window of PMU measurements, and it detects the evolving instabilities by predicting future samples and investigating the computed Koopman eigenvalues. This approach is also able to identify alarm conditions, which include slowly evolving instabilities that may not be detected by predicting future samples in a limited time horizon. Identifying these conditions provides additional time to prepare a proper set of emergency control actions to be performed when necessary. Using the proposed method, groups of coherent generators that play a role in the evolving instabilities can also be identified, contributing to the design of a defensive islanding scheme for unstable cases. The efficacy of the proposed approach is demonstrated by simulating its performance with three test systems of different scales. Economical operation condition of the power system and its reliability are two contradicting issues. Reliable operation of the power system can lead to a high-cost operation, while economical operation of the power system might result in an unreliable operation of the power system. In this thesis, a novel methodology for the optimal power flow in a power system is proposed to ensure its reliable and cost-effective operation. The methodology adopts a risk-constrained optimal power flow and develops an efficient procedure to design corrective control actions including load shedding and mechanical torque reduction of generators in emergency conditions using reinforcement learning (RL). Reinforcement learning is a type of decision making tool which enables us to determine a set of proper control actions for different operating conditions and contingencies and to implement them in real-time. Since the training process of the RL-based agent is excessively time-consuming for large power systems, because of the enormity of their actions' spaces, an approach based on dynamic mode decomposition which limits the action space during the training process of agent is proposed. The proposed scheme is implemented on two test systems including a small-sized two-area power system and the 127-bus WSCC test system. A considerable amount of operating costs of the power systems corresponds to the fuel cost of the generation units. Therefore, fuel-cost minimization of the power system plays a crucial role in the economic operation of the power system. Furthermore, various faults and contingencies on the power systems might cause irrecoverable results such as widespread blackouts and following loss of money. Considering both fuel cost and reliability level of the system, it can be concluded that it is crucial to provide an optimal power flow solution with acceptable reliability for a given loading condition. Accordingly, the risk level of the system's operating points should be investigated properly. In this study, instead of rotor angle trajectory-based severity indices, the cost of the emergency control action is taken as a severity of the contingency. Using the cost of emergency control actions provided by the trained reinforcement learning-based agent as risk of the operating point, a risk-based optimization problem has been formulated. Two optimization techniques are employed to find the solution of the formulated optimization problem. The first one is Genetic Algorithm, GA, which is one of the well-known populated-based optimization techniques and the second one is Hooke–Jeeves method which is one of the well-known examples of pattern search local approaches. In these algorithms, the candidate solutions are evaluated with both cost function and constraints. The optimum operating points with and without risk constraints has been obtained for the two area and 127-bus test systems using both Genetic algorithm and Hooke-Jeeves method and the results are discussed.
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ÖgeDeep learning for wind energy systems using the hurst exponent and statistical parameters(Graduate School, 2021-08-14) Alafi, Behnaz ; Şeker, Şahin Serhat ; 504181008 ; Electrical Engineering ; Elektrik MühendisliğiAs we all know, energy demand is continuously increasing because of population growth and developing technology. As a result of this increasing demand, energy shortages and environmental pollution will occur. Besides, because of the growing crisis and other critical issues around energy, renewable energy is taking countries' attention and becoming important in various parts of the entire world. Wind energy, solar power, tidal energy, geothermal energy, etc. as renewable energy sources have been used to solve these issues. Among these alternative sources of energy, wind and solar energy have got the most attention recently. Since wind power has less pollution, shorter construction time, less occupation, and flexible investment, it has become one of the most effective sources of energy. And in this study, the information is about wind data. But the wind is unstable and mainly affected by meteorological and navigational conditions and the principle for its implementation changes from one place to another. These changes in the meteorological measurement cause uncertainty in wind farms' generated power that affects power supply and quality. Also, because it is impossible to generate every power amount by wind energy or store electrical energy, there is a limitation on the amount of output power. Therefore, An accurate prediction can cause the cost of power generation reduction, less winding reserve capacity of the grid, and more reliable operation of the grid. Because of aforesaid reasons, prediction in wind energy systems is a very important issue. Nowadays, deep neural networks have been considering for prediction problems. In this study, the convolutional neural network(CNN) as a deep neural network is used to do predictions in wind energy systems based on meteorological data of one station. Since the Hurst exponent H is used to determine the predictability degree of a set of data, it gives some information about data that is useful in developing predictive models both theoretical and computational in nature. We first aim to apply the Hurst exponent method on wind energy data and then execute a deep neural network on data to tarin data through that deep neural network. Work steps: this literature study on the yearly meteorological features of one station applies deep learning methods to it. First of all, we gathered reported data for wind speed, air pressure, and relative humidity as the inputs of one deep neural network to train that network for predicting wind speed data. Since the power of one turbine is related to wind speed value, studying the wind speed behavior of one location leads to the study of the power capacity of that location. Before training a neural network, it is better to study the behavior of wind speed and find its statistical model and predictability degree, so before entering meteorological data into a deep neural network we studied statistical parameters of wind speed and find the probability density of it and then we found Hurst exponent, as the factor for predictability degree, and, then, all data is entered to one CNN to tarin that network and predict wind speed data.
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ÖgeDNS big data processing for detecting customersbehaviour of isp using an optimized apache spark cluster(Graduate School, 2022-02-03) Alkhanafseh, Yousef ; Akıncı, T. Çetin ; 504191100 ; Electrical EngineeringDuring the past few decades, technology fields, especially Internet of Things (IoTs),have surpassingly evolved which in turn have contributed to great proliferation of datasources. Unfortunately, at that time, the available data processing tools in terms of va-riety and advancement were insufficient to analyze that huge data in a reasonable time.They suffered from several problems such as slowness, lack of comprehensiveness,limit size of clusters, high expense. These problems have constituted major obstaclesfor the progress and achievement in Big data field. Therefore, data has been unemployedfor a while. However, when its enormous benefits such as making smart decisions,saving time and cost, monitoring servers, improving performance, minimizing hiddencorrelations, and providing high quality reports have been closely realized, process-ing big data started to be prevalent. When dealing with big data, the most famousquestion that can be asked is "how can big data analysis make the enterprise jobs andbusiness better?". Currently, huge amounts of structured and unstructured data-sets,called as big data, have started to be processed by different types of companies suchas telecommunications, software and hardware, marketplaces, social media and so on.The current advanced services, hardware, and software have played an important rolein promoting big data processing by making its analysis faster, easier and inexpensive.It is important to know the difference between big data and traditional data sources.The main difference between them can be clearly noticed in data size, types, frequency,capturing speed, and used processing tools. Despite the current advanced technolo-gies, processing ExaByte (EB) or even YottaByte (YB) of data in an efficient way thatincludes the optimal usage of used system by completely utilizing its precise features isstill a challenge and need an expert who has a good mathematical background, knowl-edge of statistics, and superior experience in this field. Based on that, this thesis aims toprovide a comprehensive approach of setting up a system that consists of three differentstages which are collecting, processing, and visualizing huge amount of DNS data,daily of 1.3 TB, using an optimized YARN-based Apache Spark cluster. The process isachieved in two different clusters in terms of their place of establishment. The first onewas established on cloud by using Amazon Web Services Elastic MapReduce (AWSEMR) and the other one was established on local machines using Apache Ambari.Nevertheless, in this project, just the cloud cluster was discussed and reported in detail.The main goal of the one who was on cloud is to determine the features of neededmachines for local cluster. Moreover, it adequately made the understanding of ApacheSpark various configurations easier by trying each one of them with different values.Additionally, different structures of Python codes, especially related to Pyspark, weretried in different ways in order to specify the most efficient one. Initially, the thesisstarts by stating an extensive introduction that takes into consideration different sub-jects such as big data concepts, properties, sources, importance, future, limitations,challenges, and processing tools. Moreover, the architecture of the used DNS servers was thoroughly explained by stating their general purpose and their working principle.Similarly, under the title of data collecting, the project's main big data, DNS, andthe other used data-sets, which are Call Detail Record (CDR), Customer RelationshipManagement (CRM), Carrier-grade Network Address Translation (CGNAT), and IP-Blocks, were distinctly clarified by representing a sample of each one in separate tables.All these data-sets are encrypted and only the concerned authorities can understandits content. Then, an additional data-set that was captured from internet websites wasintroduced by representing a sample of it. A web scraping method has been talkedabout as well. There were more than one thousand URLs which can be classified inalmost 31 categories including education, games, VPNs, Services, banks, economy,etc. After that, several services that are utilized to process the data such as ApacheSpark, Yet Another Resource Negotiator (YARN), Hadoop Distributed File System(HDFS), ZooKeeper, and Hive were briefly investigated by interpreting their impor-tance, working principle, architecture, and main configurations. Meticulously, ApacheSpark is the data processing engine in this project. On the other hand, HDFS and Hivewere used as general storages to save processed data-sets and metadata, respectively.Zookeeper is a service that is utilized in order to maintain centralized configuration in-formation and provide distributed synchronization. Other services such as AWS EMRand AWS s3 were also used in this project. AWS EMR is a platform that Apache Sparkclusters can be built on. AWS s3 is a cloud storage that was temporarily used for savingprocessed data-sets. Next, based on different factors, the differences between ApacheSpark APIs, which are Resilient Distributed Data-set (RDD), Dataframe, and Dataset,were concisely illustrated. Subsequently, a procedure of optimizing a YARN-basedApache Spark cluster was proposed by interpreting the used mathematical equationsand giving a detailed example of how to start the object of Apache spark in an optimalway. Both Apache Spark and YARN configurations that are related to applicationproperties, run-time environment and networking, shuffle behavior, compression andserialization, memory management, and execution behavior were extremely elaborated.Next, various experiments of processing data were done by using different cluster sizesthat started from small number of machines with a small amount of resources of RAMand vCores to huge ones with high number of machines and large amounts of RAM andvCores. These clusters were optimized based on the previously stated configurationsand the values that can be found on both Resourcemanager and Spark admin interfacewere exactly the same as the calculated ones that are related to the amount of RAM,number of vCores, number of containers, and parallel tasks which in turn confirms theefficient use of the available resources. As a result, about %95 of RAM and CPUs ofthe clusters were successfully utilized. On the other side, the results of the experimentswhich contain input data size, number of operations, execution time, and output datasize were efficiently reported. Based on these results, a local cluster that has the samefeatures of the most appropriate cluster that was obtained in the experiments, is locallyestablished. After that, the output DNS data was grouped based on specific schemaand saved in a compressed format which is Parquet that reduces the size of the dataapproximately four times. Then, it was transferred to an optimized Elasticsearch clusterwhich is established in order to make fast queries to the output data and visualize it byusing an interactive Kibana dashboard. The Elasticsearch cluster includes one masternode and two slave nodes. The indices of Elasticsearch were properly configured andsplit into small indices. Also, they were defined in a way that only uses needed featureswhich in turn leads to enhance and tune the work of disks. Captured visualizations have played a major role in determining useful information such as the situation of DNSservers, customers segmentations, distribution of DNS traffic across Turkey neighbor-hoods, types of customers, most visited categories, most used URLs, and suitable placesfor advertising. Eventually an application that is based on time siers forcasting wasmade. A sample of the output data was prepared to be used in a time series forecastingusing Facebook Prophet model which were selected after trying several models such asautoregression (AR), Seasonal Autoregressive Integrated Moving-Average (SARIMA)and Vector Autoregression (VAR). However, only a comparison between VAR andFbprophet is discussed in this project. The main target of this prediction is defining thedensity of the used DNS servers, giving information about missed data, and providingapproximate information about the future of servers. The models were evaluated bycomparing the test data-set with prediction one and calculating its mean absolute error.It was almost %2.49 for Fbprophet. In short, some of this thesis achievements can beconcluded as providing solid knowledge about cloud computing systems and big datadifferent processing tools, performing various experiments on different clusters withdifferent sizes and resources, establishing local cluster based on these experiments,transforming daily of 1.3 TB of raw data into meaningful information, and making asystem for processing new data continuously. Furthermore, these processed informa-tive DNS data is used in a wide range of fields such as congestion prediction for DNSservers, classifying customers, enhancing content delivery network of some specificwebsites, running successful market advertising campaigns.
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ÖgeElektrik enerji sistemlerinde güvenli işletim koşullarının elde edilmesine yönelik akıllı yöntem geliştirilmesi(Lisansüstü Eğitim Enstitüsü, 2023-06-07) Akdeniz, Ersen ; Bağrıyanık, Mustafa ; 504062002 ; Elektrik MühendisliğiGeniş alanda etkili olan elektrik enerjisi kesintilerinin en temel sebeplerinden birisi kritik kısıtlılıklardan kaynaklanan zincirleme açmalardır. Ancak, kritik kısıtlılıkların analizi olağan sistemsel arızaları ile hatalı manevra, olumsuz hava koşulları ve kasıtlı saldırı nedeniyle oluşan öngörülemeyen arızalar gibi çeşitli unsurları içerdiği için oldukça karmaşık bir süreçtir. Literatürde yer alan çeşitli analiz yaklaşımlarında söz konusu problem genellikle tek bir boyutuyla ele alınmıştır. Bu tez çalışması kapsamında, işletmesel ve işletmesel-olmayan unsurlardan kaynaklanan indisler elektrik sisteminin zafiyet (güvenlik açığı) analizi için tanımlanarak oluşması muhtemel zincirleme açma sonrası oluşacak kısıtların öngörülmesine yönelik yeni bir yaklaşım getirilmiştir. Temel olarak, elektrik sistemine ait elektriksel parametrelerden oluşan işletmesel performans indisi, kasıtlı saldırı ve kötü hava koşulu indisleri örnek test sistemleri üzerinde ayrı ayrı tanımlanmış olup, sonrasında bulanık çıkarım sistemi kullanılarak bütünleşik bir toplam zafiyet indisi tanımlaması yapılmıştır. Geliştirilen yöntem MATPOWER veri tabanında yer alan IEEE test ağları üzerinde uygulanabilecek şekilde geliştirilmiş olup, örnek test ağları için analiz ve değerlendirme çalışmaları yapılmıştır. Problemin numerik analizine ilişkin olarak her bir kısıtın neden olacağı etkiyi belirlemek için test ağları veri seti üzerinde çevresel etmenlerin (olumsuz hava ve kasıtlı saldırı) değerlendirilmesine yönelik etki katsayısı tanımlamaları yapılmıştır. Buna göre sistemi oluşturan hat ve bara gibi elemanların işletmesel, olumsuz hava koşullarında ve kasıtlı saldırılardan etkilenme sıralamaları yapılmıştır. Ayrıca, yapılan analiz çalışmaların daha sistematik bir şekilde gerçeklenebilmesi ve analiz sonuçlarının görselliğinin artırılması için MATLAB tabanlı Zafiyet Analiz Programı (MATVAP) geliştirilmiş olup, programın kullanıcı arayüzü detayları tezin ekler bölümünde sunulmuştur. İletim sistemi işletmecileri artan tehdit unsurlarına karşı acil durum manevra planlarını güncelleyerek uygulamak noktasına gelebilmektedirler. Olumsuz etkileri asgari seviyeye indirgeyecek etkin bir karşı savunma planın oluşturulabilmesi için sistemin potansiyel zafiyet oluşturulabilecek noktalarının önceden tespit edilerek, bu bilgiler ışığında savunma planlarının hazırlanması gerekmektedir. Tez çalışmasının devamında, iletim sistemi işletmecilerinin acil durum manevra planlarına katkı sağlamak amacıyla sistemde yer alan kritik noktaların tespiti ve kısıt sonrası olumsuz etkilerin azaltılmasına yönelik karşı tedbir (manevra) işlemlerini belirleyerek oluşması muhtemel teknik ve ekonomik sorunları asgari seviyeye indirgeyecek bir bilgi tabanlı karar destek algoritması geliştirilmiştir. Söz konusu problemin çözümüne yönelik olarak genetik algoritma temelli zafiyet tabanlı kısıtlılık koşullu yük akışı algoritması ile sistemin kısıt sonrası durmunu değerlendiren karar ağacı bazlı karar destek metodolojisi geliştirilmiştir. Bu sayede sistemde oluşan ilk kısıt sonrası, kısıt tipine göre müdahale planları gözden geçirilmekte ve sistemin savunma planı en az olumsuz etkiye neden olacak şekilde muhtemel ikincil kısıtlar uyarınca revize edilmektedir. Kısıt sonrası sistemde oluşacak yan etkiler; üretim artışı/azaltması ve/veya yük atma gibi kısa dönem kontrol müdahaleleri ile mevcut sistem kısıtları içerisinde kalacak şekilde çözümlenmeye çalışılmaktadır. Ancak, üretim tüketim dengesi sistem geneli için hiçbir şekilde sağlanamadığı durumlar için kısmi ada çalışma durumuna geçilebilmektedir. Yapılan çalışmanın en önemli katkılarından birisi de durağan durum için geliştirilen bir yük akışı analiz yönteminin karar ağaçları ile birlikte kullanılarak sistemin kararlılığı gibi dinamik bir olayı kestirmek için kullanılmasıdır. Geliştirilen yöntemin doğrulaması örnek bir ardışıl açma senaryosu ile EMTP programı üzerinde IEEE-39 test ağı benzetim modeli kurularak yapılmıştır. İlave olarak, sistemin güvenli moda geçmesi öncesinde maliyet analiz yapılarak söz konusu işletmesel değişikliğin yapılıp yapılmayacağı konusu ekonomik kıstaslar uyarınca değerlendirilmiştir. Geliştirilen yöntemin pratik uygulama olanağı bulması sonrasında özellikle iletim sistemi operatörlerinin acil durum manevra planlarını test etmeleri için faydalı bir araç olabileceği değerlendirilmektedir.
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ÖgeEvaluation of dielectric performance of high-temperature vulcanizing silicone rubber samples(Graduate School, 2023-01-19) Bilgiç, Taylan Özgür ; Kalenderli, Özcan ; 504191050 ; Electrical EngineeringElectricity has become a must-have rather than a need in our current times. In addition to holding a very important place in people's daily lives, it is also a great need in industrial facilities. An unplanned power outage causes huge financial losses for industrial facilities. Therefore, it is necessary to minimize power outages and to ensure a continuous generation, transmission and distribution of electricity. One of the reasons for power cuts is due to the material used. Interruptions occur due to faults in the distribution and transmission networks of electricity from the generation stage until it reaches more users. In the selection of the materials used here, it is necessary to choose according to the place and conditions where they will be used, and attention should be paid to their lifetime. In addition, when the materials used have better properties, these new materials should be used to prevent future failures. Insulators are used in transmission lines to provide insulation between the energy part and the ground. If there is a problem in one of the insulators in a transmission line, high short-circuit current will be drawn as there will be a short-circuit and a malfunction will occur in the system. This brings about the necessity to pay attention to the lifetime of the insulators and to be aware of the innovations. For this reason, traditional insulators, which are ceramic and glass ones, are replaced by silicone insulators. Silicone insulators are preferred because of their hydrophobic properties, their lightness, their resistance to impacts, their cheapness, ease of installation, protection of their properties at wide temperatures and electrical resistance. Malfunctions in insulators are generally caused by short-circuit currents due to environmental conditions, namely weather conditions such as rain, fog and snow. The reason for this is that the dirt accumulated on the surfaces of the insulators creates a conductive path together with the water formed on the surface due to these weather conditions. When this conductive path is created, a short circuit occurs and short circuit currents occur. Silicone insulators can help prevent this thanks to their hydrophobic properties. The flow of water from the surface of a silicone insulator that has not lost its hydrophobic feature does not form a path, it flows drop by drop. In this way, the formation of short-circuit current is prevented. In this study, high temperature vulcanizing (HTV) silicone rubber samples were investigated in 3 different experimental setups. The first experiment is the Inclined Plane Experiment. With this experiment, the trace and erosion resistance of HTV silicone samples are examined. The experiment was carried out in 3 different voltage types as AC, –DC and +DC and they were compared. For AC, –DC and +DC voltages, 4.5, 3.15 and 2.45 kV voltage values were tested, respectively. According to these voltage levels, the pre-resistances and the contaminant liquid flow rate were determined. A total of 5 samples has been used simultaneously in the experiment. In addition, the temperature measurements of the samples for 6 hours were taken with the help of a thermal camera. In the same way, leakage current data were obtained using the labview program. The second test was the corona discharge test. In this test, the hydrophobicity properties of HTV silicone samples were investigated. In this test, AC, –DC and +DC voltage types were tested in the same way. The voltage level required to create a corona discharge has been found through trials. 5 kV in AC voltage, 21 kV in –DC and +DC voltage was applied. In addition, tests were carried out at different temperatures and different pressures to examine the effect of ambient conditions on hydrophobicity. For each test, 2 samples were used and corona discharge was applied with needle electrodes at 3 points determined on each sample surface. As long as the discharge was applied to these 6 points and afterwards during the recovery of hydrophobicity, the roofs were photographed by dripping water drops at different times. In these photographs, the change of hydrophobicity was examined by finding the angles between the drop and the surface with the help of the program. This change was examined first as loss and then as recovery. As the third test, the dynamic drop test was performed. In this test, the hydrophobicity properties of HTV silicone samples were also investigated. In this test, AC, –DC and +DC voltage types were tested in the same way. A voltage level of 6 kV has been applied in 3 voltage types. Five samples were used for each test. In this test, samples are subjected to electrical stress with the help of 2 electrodes. A liquid is run over the surface of the samples. As a result of electrical stresses, samples lose their hydrophobic properties over time. While at first no accumulation or water path is formed on the surface of the samples during the liquid flow without losing the hydrophobic properties of the samples. As time passes and they start to lose their hydrophobicity, water drops form on the sample surface. Then, when they completely lose their hydrophobicity, a water path is formed. The innovative approach of this study is to use 3 different tests to examine the properties of HTV silicone rubber samples and to perform these 3 different tests at AC, –DC and +DC voltage types. But as a more important innovation, testing at different temperatures and different humidity is performed to examine the effect of ambient conditions in the corona discharge test. Insulators in transmission and distribution lines are located in the open air and are affected by the changes in air conditions. By performing tests at different temperatures and different humidity values and examining the hydrophobic behavior of the samples, information can be obtained about the hydrophobicity properties of silicone insulators under various climate environments including the characteristics of seasons such as summer and winter. When the inclined plane test was performed at 4.5 kV AC voltage, all 5 samples lasted 6 hours and passed the test. In the inclined plane test performed at AC voltage, the average temperature of the 5 samples was measured as 81.5 ˚C and the average of the maximum temperatures of the 5 samples was found to be 113 ˚C. At most, the 2nd sample reached a temperature of 133 ˚C. The average mass loss of 5 samples is 0.0496 grams. In the inclined plane test performed at 3.15 kV negative DC voltage, all 5 samples survived for 6 hours and passed the test. The average temperature of the 5 samples was found to be 242.81 ˚C and the average of the maximum temperatures of the 5 samples was found to be 549.45 ˚C. The 3rd and 4th samples reached a temperature of 670.09 ˚C, which is the highest temperature that can be measured. The average mass loss of 5 samples is 0.0828 grams. In the inclined plane test performed at 2.45 positive DC voltage, only the first sample survived for 6 hours and passed the test. The other 4 samples failed in less than two and a half hours because their erosion length exceeded the value specified in the standard. The first sample, on the other hand, did not cross the erosion length limit of 2.5 cm at the tip of 2.45 cm. But the greatest mass loss is in the 1st sample. The reason for this is that it has been dealing with a great erosion both transversely as well as longitudinally. The average mass loss of 5 samples is 0.85 grams. The mass loss of the 1st sample is also the highest with 1.23 grams. The average temperature value of 5 samples was found to be 98.95 ˚C. The average of the maximum temperatures of the 5 samples is 648.37 ˚C and the 1st sample has the smallest maximum temperature with 602.64 ˚C. As can be seen from these results, the best results were found at AC voltage and the worst results were found at +DC voltage. Recovery of hydrophobicity for HTV SIR samples in CDT for all 3 voltage types is best in high temperature, ie 30 °C temperature and 54% humidity ambient conditions. In the recovery of hydrophobicity, the worst case in all three voltage types is at low temperature, that is, at 18 °C and 54% humidity. In hydrophobicity loss, the worst ambient condition was found to be high temperature in all three voltage types. The best condition for loss of Hydrophobicity in AC and positive DC voltage is low humidity, ie 24 ˚C temperature and 45% humidity. The best condition for loss of hydrophobicity at negative DC voltage is low temperature. Although the samples tested at high temperature gave the worst results in terms of hydrophobicity loss, the hydrophobicity loss rate is lower than the recovery rate. So the loss is more, but the recovery is even more. In the dynamic drop test, the lowest time for the 2nd sample at AC voltage is 116 minutes, the highest time is 212 minutes for the 4th sample, and the average of the 5 samples losing their hydrophobicity is 157.4 minutes. The lowest time at negative DC voltage is 45 minutes for the 2nd sample, the highest time is 239 minutes for the 4th sample, and the average of the 5 samples losing their hydrophobic properties is 124.2 minutes. At positive DC voltage, the lowest time for the 5th sample is 75 minutes, the highest time for the 2nd and 3rd samples is more than 720 minutes, and the average of the 5 samples losing their hydrophobic properties is 387.2 minutes. As can be seen from these results, the best results were found at +DC voltage and the worst results were found at –DC voltage. The time for the samples to lose their hydrophobic properties at AC voltage is close to each other and the standard deviation is the lowest with 42.34. Although the best results are obtained at +DC voltage, there is a great difference between the loss of hydrophobic properties of the samples.
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ÖgeFerroresonance fault detection in electric power networks by artificial neural networks(Institute of Science and Technology, 2020-07) Kulaklı, Gizem ; Akıncı, Tahir Çetin ; 650079 ; Department of Electrical EngineeringFerroresonance is a complicated nonlinear waving which can appear in electrical circuits with a series or parallel connection of nonlinear inductance and capacitance. Cause of the current of ferroresonance on the transmission line elements such as cables or transformers can be partially or completely damaged. This destruction not only creates huge material losses on the system but also creates unjust suffering. It is important for the sustainability of the system that a devastating error such as ferroresonance can be detected. If ferroresonance can detecting in advance prevent the loss of time and money for the user by destroying the elements such as power transformer and cables used in the system Ferroresonance is nonlinear situation and learning in artificial neural networks has advantages such as working with missing or uncertain data, processing real conditions, handling nonlinear situations, being more successful than traditional methods, fault tolerance. Artificial neural networks are referred to by this name because they are based on learning of the human neural cell in principle. One nerve cell receives information from other cells from the dendrites department, which corresponds to input in artificial neural networks, while axon in human nerve cells corresponds to output in artificial neural networks. Artificial neural networks mainly consist of three layers. There are hidden tabs determined by the number of layers between the input and the output. The learning process is multiplied by the randomly assigned weight value of the input value, and the NET value is created, and if it is determined, the bias others are summed and output from the cell where this total value is found according to the activation function. This output value is the input of the next hidden layer and continues until the same process reaches the output value. The output value gives the result of the learning operation according to the specified value ranges. The activation function is important in solving the problem used. Various activation functions are mentioned in the thesis. A successful algorithm was investigated by using an artificial neural network method to detect ferroresonance error. In this study, four different ferroresonance data emerging with different scenarios in the transmission line which used energy transmission line modeling from western Anatolia Turkey Seydisehir-Oymapınar transmission line has 380 kV were used as input values. Work steps; literature search on the subject, detection of the moment when ferroresonance starts in voltage outputs, creating input, training and example data from ferroresonance data, to create the appropriate algorithm for nonlinear ferroresonance.
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ÖgeGeneration and measurement of mixed voltages, investigation on electrical discharge phenomena, and electric field analysis(Graduate School, 2022-04-27) İspirli, Mehmet Murat ; Kalenderli, Özcan ; 504182005 ; Electrical EngineeringThe insulation systems in power systems are frequently faced combinations of the operational voltage with over voltages. These types of voltage are called as "composite voltages" and "combined voltages" depending on the type of test object. They are superimposed two voltage signals with different properties (amplitude, frequency, time parameter, waveform). In order to generate them, it is necessary to connect different types of voltage generators together or types of two different voltage must be applied simultaneously to the device under test (DUT). In literature, tests of electrical insulation material are only applied for a single type of voltage wave. But, insulation of the system is forced with the electric field formed by the sum of the system voltage and overvoltage, when the internal and external overvoltage occurs in power systems. For example, insulation of the system is stressed with sum of the operation alternative voltage and lightning impulse voltage, when lightning strikes to power system line. During this event, the stress to which the insulation system is subjected differs according to the polarity of the lightning impulse and the polarity of the operating voltage at the time the lightning occurs. So, composite voltage conditions in the system must also be considered, when the insulation security and reliability of the system is defined. In this context, this thesis is based on three SCI articles on composite and combined voltage. In the first section of the thesis, 66 kV and 110 kV SiR insulators currently used in power transmission systems have been analyzed under combined AC–DC voltage using the finite element method (FEM). Insulators are the most crucial part of power systems. The insulation performance of insulators is vital for the sustainability of power systems. Recently, silicone rubber (SiR) insulators are used frequently in all sections of the power systems. In the analyzes made, positive and negative DC voltages in different amplitude ratios were superimposed over the phase-earth operating voltage of the insulator. In the study, the models were created based on time and analysis were applied in time-dependent. Alone DC voltage was applied to the insulator for the first 60 s, AC + DC voltage was applied between 60 to 120 s. Thus, the electric field behavior of the SiR insulator under combined AC–DC voltage has been obtained. The change of electric field based on positive and negative DC components was investigated. As a result of the study, the effect of the polarity of the DC component in the combined voltage was observed. The effect of the polarity of the DC component in the combined voltage on the maximum electric field intensity was observed. In the second section of the thesis, the effects of different electric fields, distance between electrodes and DC component of composite voltage on the breakdown voltage of air were investigated. The valve side of the converter valve in the HVDC transmission systems is subjected to mixed voltages such as composite AC & DC voltage. Normally, their structures have the geometry to create a uniform or less uniform electric field, but sharp points such as burrs on smooth surfaces can create non-uniform electric fields. In this study, four different electrode arrangements were used in the experiments to create different electric fields. The effects of the homogeneity of electric field on breakdown voltage were investigated for different ±DC component amplitudes of the composite voltage. The field efficiency factor was calculated using mean and maximum field strengths for all of them. Variation of breakdown voltage of air was examined under the composite AC & DC voltage for different ratios ±DC. As one result of the study, the breakdown occurs at the positive half-wave of the AC voltage despite −DC voltage being applied due to positive corona discharge pulses. This breakdown point is named as the polarity change point. The breakdown voltage increases with the decrease of DC voltage component up to polarity change point in non-uniform electric field. As a result of the experiments, it was seen that the polarity change point is closely related to the homogeneity of the electric field. As the homogeneity of the electric field increases, the polarity change point starts to be lower −DC voltage. In less uniform electric field, the AC breakdown voltage was measured slightly higher than the DC breakdown voltage. In less uniform electric field, as the ratio of the applied AC voltage to DC voltage increases, the breakdown voltage gradually approaches the AC breakdown voltage. This result is similar to the result obtained for the +DC component in non-uniform electric field experiments. In the last section of the thesis, firstly, experimental circuits were designed to generate and measure composite DC and LI high voltage using a simulation program. The voltage sources used in composite voltage generation must be isolated from each other with coupling elements so that they do not affect each other. In this context, it is critical to decide on the types and values of coupling elements. The coupling elements used were chosen according to simulation results. Afterward, experimental circuits were established in the laboratory according to the simulation results of the designed experimental circuit. Then, breakdown voltages under composite DC and LI voltage for less uniform and non-uniform electric fields were measured with four different electrode systems for positive and negative DC voltage pre-stresses with different amplitudes. The 50% breakdown voltage was calculated using the least-squares method. Finally, 3D models were created for the electrode systems used in the experiments using the finite element method. The efficiency factors of electrode systems calculated with the FEM results were correlated with the experimental breakdown voltage results. Thus, the breakdown behavior of air under bipolar and unipolar composite voltages (CV) was investigated. In conclusion, the experimental results showed that very fast polarity change in bipolar CV causes higher electrical stress compared to unipolar CV.
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ÖgeGüneş enerjili uçaklarda farklı uçuş durumları için elektrik sisteminin performans analizi(Lisansüstü Eğitim Enstitüsü, 2023-02-07) Ak, Ümit ; Usta, Ömer ; 504191097 ; Elektrik MühendisliğiTeknolojinin ilerlemesi ile birlikte elektrik enerjisi günümüzün en temel gereksinimlerinden biri haline gelmiştir. Bu enerjinin kullanımı ile birlikte tasarımlar daha kompakt bir hale gelmiştir. Elektrik enerjisi temelde üretim, iletim, dağıtım ve tüketim olmak üzere dört temel başlıkta incelenebilir. Bu temel başlıklar elektriksel tüm yapılara uygulanabilir. Bu yapılar, pili ile çalışan ampul devresi kadar küçük, ülkelerin birbirleri ile enterkonnekte yapıda bulunan şebeke sistemleri kadar büyük olabilir. Bu durum kara araçları, deniz araçları ve hava araçları için de geçerlidir. Günümüzde araçlarda itki kuvvetini (mekanik enerji dönüşümü) oluşturmak için fosil yakıtların kullanımı yaygındır. Bu durum da karbon salınımını arttırarak küresel ısınmaya sebebiyet vermektedir. Küresel ısınmanın önüne geçmek için tüm dünyada fosil yakıt kullanımı yerine yenilenebilir enerji kaynaklarına yönelim sağlanmaktadır. Yürütülen çalışmalar, kara, hava ve deniz gibi farklı alanlarda yenilikçi tasarımların ortaya konulmasını sağlamıştır. Bu tasarımlarda yenilenebilir enerji kaynaklarının kullanımı ile beraber, karbon salınımını azaltmanın yanı sıra hava kirliliğini azaltmak ve gürültü kirliliğini azaltmak gibi sebepler de amaçlanmaktadır. Geçmişte, araç tasarımlarında elektrik enerjisi kullanılırken zamanla araç fiyatlarının yüksek olması, kısa sürüş mesafesi, beygir gücü, elektriğe erişimin zor olması gibi nedenlerle yerlerini uzun bir süre fosil yakıtlı araçlara bırakmışlardır. Günümüzde enerjinin verimli kullanımına yönelik olarak hibrit sistemler ön plana çıkmaktadır. Temelde fosil yakıt kullanımını destekleyen bu yapıların yerini yakın gelecekte tam elektrikli araçlara bırakacağı düşünülmektedir. Küresel ısınmaya bağlı olarak dünyada iklim değişiklikleri yaşanmaktadır. Bu iklim değişiklikleri sonucunda yeryüzü sıcaklığı yıllar geçtikçe artmaktadır. Yaz aylarındaki sıcaklık artışı orman yangınlarını tetiklemektedir. Bu çalışmada, hem yaz aylarında orman yangınlarına karşı gözlem yapabilen hem de farklı amaçlarla (hava olayları hakkında veri toplamak, vahşi hayvanların doğal ortamdaki hareketlerini izlemek gibi amaçlar) kullanılması düşünülebilen bir güneş enerjili hava aracının elektriksel ön tasarımı ortaya konulmuştur. Ön tasarımı yapılan hava aracının yaz aylarında (Haziran, Temmuz ve Ağustos) sürekli olarak uçuş gerçekleştirebileceği düşünülmüştür. Bu durum göz önüne alınarak farklı uçuş durumları için incelemeler yapılmıştır. Teknolojik gelişmeler ve malzeme biliminin ilerlemesi sayesinde daha önceden kısa uçuş süresine ve alçak irtifa yeteneğine sahip hava araçları günümüzde uzun uçuş süreleri ve yüksek irtifa yetenekleri ile dikkat çekmektedirler. Ön tasarımı ortaya konulan güneş enerjili hava aracında kullanılan malzemeler ve yöntemler güncel teknolojik gelişmeler göz önüne alınarak seçilmiştir. Hava aracının tasarım kriterleri olarak; Antalya ilinde 90 gün boyunca havada kalabilmesi (yaz aylarında yangın çıkma ihtimali ekvotar bölgesine yakın olmasından dolayı yüksek), operasyonel irtifasının 60000 feet (irtifaya bağlı olarak yüksek hızlı rüzgar gelme olasılığının düşük olması), yatay eksende 55 m/s hızla sürekli olarak uçuş yapabileceği (60000 feet irtifadaki rüzgar hızına bağlı olarak yakın değer seçilmesi) ve kalkış ağırlığının 525 kg (batarya ağırlığının göz önünde bulundurulması sonucu) olduğu belirlenmiştir. Bu isterlerden hareketle uçaktaki aerodinamik hesaplamalar ve elektriksel hesaplamalar yapılarak elektriksel güç sistemi modellenmiştir. Farklı uçuş durumları için aerodinamik güç isterleri belirlenmiş olup, elektriksel olarak güç sisteminin yeterliliği ispatlanmıştır. Uzun uçuş durumunda gece ve gündüz durumu için simulasyon çalışmaları yapılmış olup, ön tasarımı yapılan hava aracının elektrik güç sisteminin hangi durumlarda yeterli olup olmadığı belirtilmiştir.
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ÖgeHavacılık uygulamaları için emniyet kritik daimimıknatıslı alternatör tasarımı ve analizi(Lisansüstü Eğitim Enstitüsü, 2022-06-03) Ersöz, Hüseyin ; Kocabaş, Ahmet Derya ; 504191024 ; Kimya MühendisliğiHavacılık motorları, hava araçlarına itki veren temel bileşen olmanın yanında barındırdıkları elektriksel güç üretim sistemleri ile platformun ihtiyaç duyduğu elektrik enerjisinin üretilmesini sağlar. Hava araçlarında güç üretim ihtiyacı, içten yanmalı motorun başlatılması için bir elektrik motoru olan marş motorlarının kullanılması ile başlamıştır. Zaman içerisinde ilerleyen teknoloji ile birlikte hava araçlarına iç aydınlatma, ısıtma ve haberleşme gibi elektikle çalışan sistemler eklenmeye başlanmıştır. Bunun yanında motor ve platformda hidrolik, mekanik ve pinomatik sistemler yerine daha yüksek verimli olan elektrikli sistemler kullanılmaya başlanmıştır. Hava araçlarının ihtiyaç duyduğu elektrik enerjisi gelişen teknoloji ile beraber günden güne artmakta ve elektriksel güç üretim sistemlerinin güç yoğunluğu giderek artmaktadır. Yüksek güç yoğunluğunu sağlamak adına elektriksel güç üretim sistemlerinde sürekli mıknatıslı alternatörler tercih edilir. Bir hava aracının havada kalabilmesi için elektrik enerjisi gereklidir ve bu enerjiyi sağlayan güç üretim sisteminin en zorlu koşullarda bile aktif olması ve hata durumlarında platforma zarar vermemesi kritik bir öneme sahiptir. Bu sebeple yüksek güç yoğunluğunun yanında elektriksel güç üretim sistemlerinin hata toleransının yüksek olması gereklidir. Böylece olası bir hata durumunda içten yanmalı motor çalışmasına devam etmeli ve platform görev süresini tamamlamalıdır. Ayrıca hata anında ve sonrasında motorda ve platformda oluşacak tahribat en düşük seviyede tutulmalıdır. Bu çalışma kapsamında hata toleransı en yüksek olan sürekli mıknatıslı alternator topolojisini belirlemek adına aynı tepe seviyede isterlere sahip gömülü mıknatıslı generatör, mıknatıs destekli senron relüktans makine ve yüzey mıknatıslı generatör tasarımları gerçekleştirilmiş olup, emniyet kritiklik, ağırlık ve üretilebilirlik bakımından karşılaştırılmıştır. Karşılaştırma sonucunda hava aracı güç sistemlerinde kullanılması en uygun olan topoloji belirlenmiş ve hata toleransını artırmaya yönelik tasarım çözümleri bu topolojiye uygulanmıştır. Alternatör analitik tasarım ve elektromanyetik analizleri sonlu elemanlar paket programları olan JMAG ve ANSYS MAXWELL ile gerçekleştirilmiştir. Çalışmanın son bölümdünde en iyileştirilmiş tasarımın rölanti ve maksimum devirdeki performansı elde edilmiştir. Ayrıca 3 boyutlu analiz ile demir ve bakır kayıpları çıkarılıp verim hesabı yapılmıştır. Ek olarak tasarımı yapılan hata toleransı yüksek, emniyet kritik alternatörü geliştirmeye yönelik öneriler sunulmuştur.
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ÖgeImplementation and comparison of super resolutionalgorithms on embedded systems(Graduate School, 2023-05-05) Akkın, Metin ; Yalçın, Müştak Erhan ; 504191216 ; Electronics EngineeringIn this thesis, we aim to implement CNN based super-resolution methods and our implementation is image and video on embedded systems. CNN based super-resolution methods are EDSR, ESPCN, FSRCNN and LAPSRN. We compared super-resolution methods based on scale factor, layers and parameters. We trained all these super-resolution methods on embedded systems. These methods are explained and applied on embedded systems. We compared interpolation-based methods, reconstruction methods and learning-based methods. We focused on performance enhancing research to achieve real-time performance and we implemented the super-resolution implementation in 2 processes. They are training process and implementation process. In training process, we used different datasets on all super-resolution methods and we produced trained files, which have all scale factor for implementation process. First of all in implementation process, we focused on choosing the programming language for better performance. We chose a super-resolution method, we produced same code on different programming languages for super-resolution implementation and we analyzed implemetation performance on different programming languages, then we analyzed PSNR, SSIM and elapsed time values on each CNN based super-resolution methods for quality. We found a trade off for quality and processing time. To achieve real-time performance, we implemented to image and video implementations on CPU and DPU and we compared the performance of super-resolution implementations on CPU and DPU. We obtained to inferences to reach real-time super-resolution implementation. We used single-core and multi-core structure on image implementation and we compared single-core and multi-core CPU performance on image implementation with using all super-resolution methods. We used a frame in single-threading and a frame which consisting of many tiles in multi-threading structure on video implementation. We created a image by stitch all the tiles on video implementation. CPU and DPU analyzes are reported for real time performance. We shared the codes of super-resolution applications and analysis on our Github account.
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ÖgeImproved tracking algorithm for rooftop pv systems employing multi-input DC-DC converter(Graduate School, 2023-01-27) Bayraktar, Gökhan ; Yıldırım, Deniz ; 504191021 ; Electrical EngineeringThe energy need of humankind has been increasing rapidly with the population and consumption increment. Various energy production methods have been investigated to meet this need since the beginning of the 20th century. After half of that century, solar energy has become one of the most studied concepts of energy production methods. With the help of economy-politics crises upon oil or natural gas, investment in non-dependent energy types has increased. Solar energy has become one of the invested areas. Here wishful thoughts may be such that the environmental risks of using fossil fuels are also one of the reasons for this tending, but it is not. The solar energy concept consists of three following main parts. Photovoltaic (PV) panels for transforming solar photon energy into DC electrical energy. Power electronics devices for MPPT implementation and manipulating the electrical power according to the load side. Lastly, the load part of the systems can be a DC load, AC load, or the utility grid directly. In this thesis, a study about the power electronics part of the concept has been completed. At the power electronics aspect, the system may have a single DC/AC converter or two-stage with a DC/DC and a DC/AC converter. As known, PV panel characteristics are not linear; therefore, a maximum power point tracking (MPPT) algorithm should be designed to extract the maximum available power from the panel. Also, to transform the PV panel's DC power into AC power, some electronic manipulation should be configured with switching mode power supplies. These main requirements can be provided within a converter that forms the single-stage PV power system or can be divided into two converters to build a two-stage PV power system. Both systems have their benefits and drawbacks. This study's content is designing a DC/DC converter of the two-stage PV power system. The main targets of the converter are implementing the MPPT algorithm and boosting the low DC voltage level of the PV panel up to 400V DC level for being transformed into AC voltage for utility grid injection. Additionally, the designed converter accepts four PV panels as its input and applies the MPPT algorithm to each one independently. The converter is named as Collector module. As a result, the Collector module consists of four small power electronics topologies whose outputs are connected in parallel to form the single high 400V DC voltage output. The input of the system (PV panels) can have various parameters between 25V to 50V voltage and up to 400W power. Thus, the total nominal output of the module is 1600W. The reason for this individual MPPT configuration is to eliminate the problems with the string-connected PV panel systems. As known, a PV panel has I-V and P-V curves due to PV cell configuration and environmental aspects such as irradiance strength and temperature. The MPPT algorithm aims to carry the PV panel operation point through these curves and locates the maximum power point. When the PV panels are serial or parallel connected to increase the system's power, these curves change according to connection configuration. However, the system performance degrades significantly if some shading effect or other problem occurs on even a single PV panel. Because in this case, the problematic PV panel is not just a lack of contribution to the total system but also has adverse effects on the power produced by other PV panels. In literature, many MPPT algorithms have been theoretically and practically examined and applied to PV system converters. They have advantages and disadvantages regarding implementation easiness, accuracy, stability, or settling speed towards ambient changes. These aspects can be calculated and predicted with theoretical methods. However, another phenomenon is named "power traps" above the I-V and P-V curves of the system. This phenomenon is caused by the interaction of PV panels and power electronics circuits. The outcome of this phenomenon is a disordered structure of a non-linear I-V curve. Such as, even though the ideal theoretical curve is not linear, its fundamental concept is that when PV voltage decreases, PV current should increase at the same irradiance strength as an inverse relationship. However, with the result of the panel and circuit integration, the resultant curve does not follow this fundamental, especially around the DCM-CCM limit. Consequently, when a regular MPPT algorithm is applied to the system, it is observed that the steady-state operation point is far away from the actual maximum power point. As a result, an improved version of the Incremental Conductance (InC) algorithm has been developed and applied to each circuit independently by a single microcontroller. This thesis mainly focuses on the system's software structure, such as designing the novel MPPT algorithm and time-shaping between the moments of required measurement occurrences for four circuits and the MPPT calculations. Lastly, these four circuits are driven with the interleaved technique by having a 45° phase shift between the consecutive circuit's PWM signals. Last, the collector module's hardware structure has been designed for this study. Push-pull topology has been used for four power circuits. The designed module has been tested in various ways. Firstly, individual power circuits were connected to a PV simulator device separately to check the MPPT accuracy. A PV simulator is an analog device whose output characteristic coincides with actual PV panels. With this device, a controllable imitation of a PV panel has been used; hence the circuits in the collector module could be tested under various input powers. According to the results, the MPPT efficiencies of all circuits are above 99%. This verifies that the designed MPPT algorithm has successfully tracked the maximum power point. On the other hand, power transfer efficiency is around 92%-93% for each circuit. Then, all inputs of the collector module were loaded at the same time to verify simultaneous power transfer. Firstly, 4 PV panels are used as inputs. Secondly, 3 PV panels and the PV simulator are used as inputs. In both cases, both MPPT and power transfer efficiency ended up with similar values to the individual test results. Consequently, simultaneous MPPT operation and power transfer are verified with these tests, as well as the availability of using different PV sources simultaneously.
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ÖgeKonvansiyonel ve mikro şebeke içeren güç sistemlerinde dinamik ekonomik yük ve emisyon dağıtımının sezgisel yöntemlerle analizi(Lisansüstü Eğitim Enstitüsü, 2022-06-17) Aydın, Esra ; Türkay, Belgin ; 504191019 ; Elektrik MühendisliğiYıllar içerisinde yaşanan nüfus artışı ve teknolojik gelişmeler ile birlikte enerji talebinde artış yaşanmaktadır. Bu artış ile birlikte elektrik enerjisi üretim sistemlerinin sayısında artış yaşanmakta ve güç sistemleri, daha büyük ve daha karmaşık bir hale gelmektedir. Talebin artması ile güç sistemlerinde yaşanan büyüme, bu sistemlerin ekonomik olarak işletilmesi konusuna büyük önem kazandırmaktadır. Bu hususta, güç sistemlerinin optimizasyon planlamalarından biri olan Ekonomik Yük Dağıtımı problemi oldukça önemli bir hale gelmiştir. Ekonomik yük dağıtımı, termik santrallerde yakıt maliyetinin en aza indirgenmesinin amaçlandığı ekonomik bir planlamadır. Bu kapsamda, güç ünitelerinin çıkış güçleri talep gücü karşılayacak şekilde yakıt maliyetinin minimum olması için optimum planlama yapılır. Bu planlama yapılırken sistemin kısıtları göz önünde bulundurulmalıdır. Güç denge kısıtları, generatör kısıtları ve rampa oranı kısıtları dahilinde en optimum planlama yapılmalıdır. Fosil yakıtların kullanıldığı güç ünitelerinde atmosfere emisyon gazları salınır. Sera gazı olarak da bilinen bu gazlar, atmosferde sera etkisine sebep olarak dünyadaki yaşamı pek çok açıdan tehdit etmektedir. Atmosferdeki emisyon gazı yoğunluğunu azaltmaya yönelik çalışmalar küresel bir boyuta ulaşmıştır. Güç sistemlerinin, emisyon yoğunluğuna en fazla sebep olan birimlerden biri olduğu düşünüldüğünde, emisyon yoğunluğunun minimuma indirilmesinin amaçlandığı ekonomik emisyon dağıtımı, önemli bir konu haline gelmiştir. Ekonomik emisyon dağıtımında, emisyon yoğunluğunun minimuma indirgenmesi amaçlanır, yakıt maliyetinden bağımsızdır. Ekonomik yük dağıtımı probleminde ise yakıt maliyetinin minimum olması amaçlanır, emisyon yoğunluğu önemsenmez. Ekonomik yük dağıtımı ve emisyon dağıtımının birlikte ele alındığı durumda ise birleşik ekonomik emisyon-yük dağıtımı fonksiyonu oluşturulur ve hem yakıt maliyetinin hem de emisyon yoğunluğunun en aza indirilmesi amaçlanır. Güç sistemlerinin ekonomik yük ve emisyon dağıtımı problemlerinin çözümünde çeşitli optimizasyon yöntemleri kullanılmaktadır. Bu yöntemler klasik ve sezgisel yöntemler olarak ikiye ayrılır. Sistemlerin büyük boyutlu olması sebebi ile klasik yöntemlerden ziyade sezgisel yöntemlerin kullanımı daha uygun olmaktadır. Sezgisel yöntemlerin karmaşık problemlere uygulanabilirliği, çözüm süresinin hızlı olması gibi sağladığı avantajlar popülerliğini arttırmıştır. Genetik Algoritma, Parçacık Sürü Optimizasyonu, Tabu Araştırma ve Yapay Sinir Ağları günümüzde uygulamalarda en çok tercih edilen sezgisel yöntemlerdendir. Bu tez çalışmasında, güç sistemlerinin dinamik ekonomik yük dağtımı ve emisyon dağıtımı gerçekleştirilmiştir. Problemlerin analizi için sezgisel algoritma yöntemlerinden olan Genetik Algoritma (GA) ve Parçacık Sürü Optimizasyonu (PSO) yöntemleri kullanılmıştır. Algoritmalar, 5 ve 10 üniteli sistemler ile mikro şebeke içeren sisteme uygulanmıştır. Algoritmalara ait kodlamalar MATLAB programında oluşturulmuştur. 5 ve 10 üniteli sistemlerin dinamik ekonomik yük dağıtımı, emisyon dağıtımı ve dinamik ekonomik emisyon-yük dağıtımı gerçekleştirilmiştir. Mikro şebeke içeren sistem için ekonomik yük dağıtımı gerçekleştirilmiştir. Uygulamada güç denge kısıtı, generatör limitleri, hat kayıpları, rampa oranı kısıtları ve valf nokta etkisi dikkate alınmıştır. Analiz sonuçları literatürde yapılan çalışmaların bulguları ile karşılaştırılmış, GA ve PSO yöntemleri ile daha optimum sonuçlar elde edildiği görülmüştür. Ayrıca bu yöntemler kendi arasında karşılaştırıldığında ise PSO algoritmasının daha uygun sonuçlar verdiği görülmüştür.