LEE- Elektrik Mühendisliği-Yüksek Lisans
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ÖgeDeep learning for wind energy systems using the hurst exponent and statistical parameters(Graduate School, 2021-08-14)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.
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ÖgeCompensation of dead time caused output voltage distortion in SPWM full bridge inverter(Graduate School, 2022-01-18)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.
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ÖgeA peak current controlled dimmable sepic led driver with low flicker(Graduate School, 2022-01-18)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.
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ÖgeDağıtım şebekelerinde aşırı akım rölesi ile adaptif koruma(Fen Bilimleri Enstitüsü, 2020-07)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.
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ÖgeFerroresonance fault detection in electric power networks by artificial neural networks(Institute of Science and Technology, 2020-07)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.