LEE- Elektrik Mühendisliği Lisansüstü Programı

Bu topluluk için Kalıcı Uri

http://hdl.handle.net/11527/19259

Gözat

Dağıtım şebekelerinde aşırı akım rölesi ile adaptif koruma

(Fen Bilimleri Enstitüsü, 2020-07) Var, Hakan ; Türkay, Belgin ; 637246 ; Elektrik Mühendisliği Anabilim Dalı

Çevreyi 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.
Ferroresonance 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 Engineering

Ferroresonance 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.
Online impedance measurement of batteries using cross-correlation technique

(Lisansüstü Eğitim Enstitüsü, 2021) Gücin, Taha Nurettin ; Ovacık, Levent ; 723130 ; Elektrik Mühendisliği

Although the foundation of battery technologies had been laid out quite a long time ago, the recent increase of the interest for technologies such as renewable energies, portable devices, electric vehicles urged the battery technology to emerge as a major research topic. Moreover, for such applications, the batteries generally considered to be one of, if not the most crucial component of the system, as the energy provided within them ensures the continuity of system operations. Additionally, the lifetime of such systems is generally directly correlated with the lifetime of batteries utilized thereof. Consequently, the assessment of battery condition is a crucial aspect for the durability and stability of a very wide range of applications. However, as most of the parameters regarding the status of the batteries are not directly measurable, the battery status generally has to be estimated. This task is generally undertaken by a battery management system (BMS), which often runs some tests on the battery to predict certain parameters such as state of health (SOH) and state of charge (SOC). Electrochemical Impedance Spectroscopy (EIS) principally presents the complex impedance values of a battery over a frequency range of interest and it is widely accepted as the most advanced testing technique for batteries as it allows a foundation for the estimation of various battery parameters such as battery temperature, SOC and SOH. However, very often, these measurements have to be accompanied with advanced estimation algorithms for ensuring reliable and accurate estimation of parameters. This thesis aims to provide a practical implementation of EIS measurements. For this purpose, the study presented in this thesis implements the suggested method into the DC-DC converters, which are almost always present in the systems deploying batteries. The implemented method is based on cross-correlation calculations. In the first part of the thesis the motivation and the objectives of the study is elaborated and a literature overview of the state of the art is provided for representing the current approaches and for emphasizing the need for improvements. The first part is followed by a theoretical background section, where the principles of the cross-correlation calculations and it's adaptation to battery EIS measurements are explained in detail. Several improvements for the method, especially aimed for battery applications, are also provided. In the subsequent part, the theoretical findings are supported by simulations, which are created in MATLAB by using the Simulink graphical programming environment. In this part, the theory is preliminarily tested by simulations regarding impedance measurements of a passive RLC circuit. In the latter part, a test bench is designed for performing experiments to serve as proof of concept for the suggested approach. The test bench comprises a digitally controlled boost converter that is configured for charging a 12-V, 7-Ah sealed lead-acid (SLA) battery. The boost converter is controlled by an FPGA based platform, namely the Nexsys 4 DDR of Xilinx. The digital controller also comprises subprogram for injecting the necessary signals to the battery. The waveforms that occur during the tests are then recorded by a data acquisition system based on NI cDAQ platform so that the saved data can be processed in MATLAB environment for calculating the EIS diagrams. During the experiments, firstly, the battery is tested via the proposed method at 50% SOC. It is shown that the results of the present approach coincide with those obtained by a commercially-available, laboratory-type, high-precision instrument. Finally, the tests were also repeated for 25% and 75% SOC values. Additionally, further results are also presented to prove the validity of the approach even when the DC-DC converter is configured to provide a constant current under closed feedback loop. In conclusion, it is shown that the proposed approach can be reliably used to analyse the impedance of batteries over a wide frequency range during battery charging process.
Adaptive signal processing based intelligent method for fault detection and classification in microgrids

(Lisansüstü Eğitim Enstitüsü, 2021) Azizi, Resul ; Şeker, Şahin Serhat ; 724566 ; Elektrik Mühendisliği

The 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.
Deep 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 Mühendisliği

As 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.
Şebeke ile senkron çalışmada çok seviyeli güç elektroniği devreleri üzerinden okyanus dalga enerjisinin dönüştürülmesinin matematiksel modellemesine katkılar

(Lisansüstü Eğitim Enstitüsü, 2021-12-19) Çolak, İlknur ; Kocabaş, Derya Ahmet ; 504062004 ; Elektrik Mühendisliği

Son yıllarda dünyadaki enerji ihtiyacının karşılanması amacıyla yapılan çeşitli araştırmalar içinde yer alan dalga enerjisinden yararlanma fikri, günümüz koşullarında her geçen gün daha da önem kazanmaktadır. Son yarım asırdır artan petrol krizi ile birlikte doğal enerji kaynaklarından elektrik enerjisi üretimi popüler hale gelmiş ve dünya genelindeki pek çok ülkenin 2030 yılı itibariyle karbondioksit ve benzeri diğer zehirli gazların emisyonunu %50'ye düşürme hedefleri sayesinde yenilenebilir enerji kaynaklarının kullanılması zorunlu hale gelmiştir. Güneş ve rüzgar enerjisine kıyasla kendine küçük de olsa bir yer edinen dalga enerjisi, konunun karmaşıklığı, üretiminin zor olması ve uygun uygulama alanı bulma konusundaki zorluklar nedeniyle halen prototip safhasını aşıp, ticari boyut kazanamamıştır. Dalga enerjisinden elektrik enerjisi üretimi konusunda yapılan çalışmalarda gelişmiş güç elektroniği topolojilerinin ve kontrol yöntemlerinin uygulanması konusunda geniş çaplı araştırmalar yapılmamıştır. Bu nedenle bu tez çalışmasında ilk olarak dalga enerjisinden Wells türbini yardımıyla elektrik enerjisi üretiminin matematiksel analizi yapılmıştır. Sistemin ve sistem bileşenlerin ayrık ve bütünleşik matematiksel modelleri elde edilmiştir. Dalgadan elde edilen enerjinin şebekeye aktarılması için üç seviyeli aktif doğrultucu ve üç seviyeli evirici kullanılmış ve sistem kapalı modellenmesi benzetim ortamında gerçeklenmiştir. Denizdeki rüzgar dalgalarının enerjisi dağıtım ağındaki elektriğe dönüşene kadar, genel olarak dört aşamadan geçer. İlk aşamada, dalgadaki enerjiyi bir mekanik harekete dönüştürmek gerekir. Ortaya çıkan bu mekanik enerji, denizdeki dalgaların karakteristiği gereği, büyük genlikli, düşük hızlı hareketlerdir. İkinci aşamada mekanik enerjinin elektrik enerjisine dönüştürülmesi gerekir. Şebekede kullanıma uygun olmayan bu enerjinin, sabit frekans ve sabit genlikli gerilime dönüştürülmesi gerekir. Elektrik üretimine uygun hızlara getirilmiş hareketten elektriğin üretilmesi ve üretilen elektriğin dağıtım şebekesine aktarılabilecek kaliteye getirilmesi de üçüncü aşamada çözümlenir. Son aşamada ise üretilen elektriğin, özellikle çok sayıda birimden oluşan sistemlerde, genel dağıtım ağı ile entegre edilmesi söz konusudur. Dalga enerjisinden enerji üretimi konusundaki en büyük zorluklardan biri olan dalga dönüşüm tipinin belirlenmesi ve belirlenen sistemin hidrolik ve pnömatik çevrim analizlerinin yapılarak sistem matematiksel modellemesinin gerçekleşmesidir. Bu çalışmada dalga enerjisi çevrim sistemi için salınımlı su kolonu (OWC) sistemi kullanılmış ve pnömatik enerjinin mekanik enerjiye dönüştürülmesi için de en basit ve güvenilir kendinden doğrultmalı hava türbini olan Wells türbini kullanılmıştır. Türbin/generatör grubunun çıkışındaki gerilimin doğrultulmasında kullanılan aktif doğrultucu, üç fazlı, üç seviyeli ortadan kenetlemeli çevirici (3L-AFE) topolojisi olup, çevirici sinüs biçimli darbe genlik modülasyonu (SPWM) metodu ile kontrol edilmiştir. Doğrultucunun çift yönlü akım akıtma yeteneğine sahip olması sayesinde, generatörden çekilen reaktif gücün kontrolü, toplam harmonik akım kompanzasyonu, generatörün ilk kalkış anındaki tahrik geriliminin sağlanması mümkün olmaktadır. Doğru gerilimin alternatif gerilime çevrilmesinde kullanılan ve gerilim kaynağı olarak çalıştırılan üç seviyeli evirici (3L-NPC), güç elektroniği uygulamalarında henüz çok yeni bir kontrol yöntemi olan üç seviyeli NTV-SV-PWM (nearest three vector - space vector - pulse width modulation) metodu ile kontrol edilmiştir. Uzay vektör modülasyon yöntemi gerilimin daha geniş bir aralıkta kontrol edilmesini sağlarken aynı zamanda cıkıştaki harmoniklerin düşürülmesinde onemli rol oynamaktadır. Bu çalışmada amaç farklı disiplinlerdeki yenilikleri bir araya getirerek yenilenebilir enerji kaynakları uygulamalarına yenilikçi bir açısı ile özgün bir katkıda bulunmaktır. Uzay vektör modülasyon yöntemi, ölçülen anlık referans vektöre en yakın üç vektörün belirlenmesi ve yarı iletken anahtarların bu vektör değerlerini oluştururken anahtarlama kayıplarını en düşük seviyede tutacak sıra ve sürede (dwell time) uygulanmasına dayanır. Bu yöntem ile evirici anahtarlama kayıpları minimunda turulurken evirici çıkışının şebeke gerilimine senkronize edilmesi de sağlanmış olur. Doğru gerilim barasının kontrolünde vektörlerin büyüklüklerindeki ve açılarındaki sapma ve doğru gerilim barası orta noktasının akım yönü bilgisi kullanılır. Bu bilgilere göre anahtarların anahtarlama sırasının önceden kestirimi yapılır ve bir veri arama tablosu (look-up table) oluşturulur. Bu sayede kontrol döngülerinin azaltılması ve sistem cevabının hızlandırılması sağlanmış olur. Aynı doğru gerilim barası ile birbirine bağlı üç seviyeli aktif doğrultucu ve üç seviyeli eviricinin görev dağılımları aşağıdaki gibidir. Aktif doğrultucu: -Alternatif gerilimin doğru gerilime çevrilmesi -Doğru gerilim barasının gerilim değerinin sabit tutulması -Çift yönlü akım geçişinin sağlaması (S-PWM kontrolü) -Akım harmoniğinin düşürülmesi -Doğrultucunun alternatif gerilim uçlarında üç seviyeli faz arası gerilimlerinin oluşturulması -Alternatif gerilim girişinden sinüzoidal akım çekerek güç faktörünün bire yakın tutulması Üç seviyeli evirici: -Doğru gerilimin alternatif gerilime çevrilmesi -Çift yönlü akım geçişinin sağlaması (NTV-SV-PWM kontrolü) -Akım harmoniğinin düşürülmesi -Gerilim harmoniğinin düşürülmesi -Doğru gerilim barası nötr noktasının kontrolü -Çıkış geriliminin şebekeye senkron çalıştırılması Bu çalışmada salınımlı su kolonu (OWC), Wells türbini, senkron generatör, doğrultucu ve eviriciden oluşan dalga enerjisinden elektrik enerjisine dönüşüm sisteminin tekil matematiksel modellemesi detaylandırılmış ve karmaşık girişli, karmaşık sistemin bütünleşik matematiksel modellerine katkıda bulunacak sonuçlar elde edilmiştir. Dalgalardan elektrik enerjisi üretimi konusuna duyulan ilgi dünya genelinde artmakta olup, çalışmalar henüz yeni yaklaşımlar üretmek düzeyindedir. Bu nedenle bu tez çalışmasının sonuçları ciddi bir dalga enerjisi potansiyeline sahip olan Türkiye'nin enerji politikalarının başarısı ve bu konuda uluslararası platformlarda yer edinmesi açısından büyük önem taşınmaktadır. Ayrıca bu proje Türkiye'nin yenilenebilir enerji konusundaki antlaşmalarda üstlendiği yükümlülüklerin yerine getirilmesi açısından da katkı sağlamaktadır.
Compensation 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 Mühendisliği

Nowadays, 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.
A peak current controlled dimmable sepic led driver with low flicker

(Graduate School, 2022-01-18) Örüklü, Kerim ; Yıldırım, Deniz ; 504181056 ; Electrical Engineering ; Elektrik Mühendisliği

Nowadays, 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.

Gözat

Çıkarma tarihi ile LEE- Elektrik Mühendisliği Lisansüstü Programı'a göz atma

Sayfa başına sonuç

Sıralama Seçenekleri