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Öğe A Hybrid MPPT method for grid connected photovoltaic systems under rapidly changing atmospheric conditions(Elsevier Science Sa, 2017) Celik, Ozgur; Teke, AhmetThe modest changes in operating current and voltage of photovoltaic (PV) panel due to the temperature and radiation fluctuation constitute visible variations in the output power. In this paper, a hybrid method to optimize the performance of the maximum power point tracking (MPPT) controller for mitigating these variations and forcing the system to operate on maximum power point (MPP) is developed. The presented Hybrid MPPT method consists of two loops: (i) artificial neural network (ANN) based reference point setting loop and (ii) perturbation and observation (P&O) based fine tuning loop. To assess robustness of the proposed method, a comparison is performed using the conventional P&O, incremental conductance (INC) and ANN based MPPT methods under both rapidly changing radiation and partially shaded conditions by using PSCAD/EMTDC program. The results obtained from the test cases explicitly demonstrate that the presented MPPT method not only achieves an increase in speed of MPP tracking, but also reduces the steady state oscillations and prevents the possibility of the algorithm from confusing its perturbation direction. The system efficiency more than 98.26%, 120 ms improvement in convergence speed and 1.16 V decrease in the rate of overshoot are obtained with proposed Hybrid MPPT method under the rapidly changing environmental conditions. (C) 2017 Elsevier B.V. All rights reserved.Öğe A hybrid random parameters modification to MPPT algorithm to mitigate interharmonics from single-phase grid-connected PV systems(Elsevier, 2022) Hussein, Ibrahem; Celik, Ozgur; Teke, AhmetAs the on-grid photovoltaic (PV) system's penetration level increases, the utility grid power quality (PQ) becomes a vital emerging issue to spot the lights on. The recent studies confirmed that one source of interharmonics emission from PV inverters is the maximum power point tracking (MPPT) algorithms. Following this issue, a strong relationship has been found between the interharmonics generating characteristics and the MPPT parameters, such as the sampling rate and perturbation step size. Utilizing a big step size and fast sampling rate will enhance the tracking performance but will raise the level of interharmonics injected into the grid and lower the overall efficiency. Therefore, there is a trade-off between MPPT parameters, efficiency, and interharmonics characteristics in PV systems. To alleviate mentioned issue, this paper investigates the mechanism of interharmonics generation and emission under different power operating conditions. Accordingly, a new mitigation technique is presented for interharmonics generation in grid-connected PV systems. The proposed method is based on modifying the MPPT algorithm in a way that maintains its performance characteristic while effectively minimizing the generated interharmonics by a random selection of big or small perturbation step sizes and fast or slow sampling rate, respectively. Thereby, the frequency spectrum distribution is modified, and interharmonics peaks are reduced in the output injected current to the grid by 27 % compared to the other conventional and modified perturb and observe (P&O) MPPT algorithms found in the literature. The effectiveness of the proposed method is verified by simulation studies on a single-phase grid-connected PV system. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Öğe A novel gene expression programming-based MPPT technique for PV micro-inverter applications under fast-changing atmospheric conditions(Pergamon-Elsevier Science Ltd, 2022) Celik, Ozgur; Zor, Kasim; Tan, Adnan; Teke, AhmetThe erratic behavior of the atmospheric conditions adversely affects efficient energy harvesting and the stable operation of photovoltaic systems. It is therefore critical to draw maximum power from photovoltaic modules regardless of atmospheric conditions. The maximum power point tracking techniques have crucial impacts on both efficient and stable operation of photovoltaic systems as being the controller part of the power converters. In this paper, a novel gene expression programming-based maximum power point tracking technique is proposed for micro-inverter applications under fast-changing atmospheric conditions. In this context, the main objective of this study is to improve the significant performance indices of maximum power point tracking technique including convergence speed during transients, tracking accuracy, steady-state oscillations, and rate of overshoots for ensuring the stable and efficient operation of the photovoltaic micro-inverter system. The proposed maximum power point tracking technique is integrated to a two-stage grid-connected micro-inverter system and tested in terms of the aforementioned performance parameters. The performance analyses of the developed technique are performed under various scenarios by utilizing the PSCAD/EMTDC platform. The obtained results reveal that the rate of overshoots is decreased by 0.6 A while the convergence speed is accelerated by 1.4 s. In comparison with traditional MPPT techniques, tracking accuracy, steady-state stability, and robustness of the whole system are remarkably improved along with increasing overall system efficiency by 4%. It is also worth pointing out that the complexity level of the control technique is significantly reduced by the equation obtained through the symbolic regression analysis.Öğe A state-of-the-art review of artificial intelligence techniques for short-term electric load forecasting(Institute of Electrical and Electronics Engineers Inc., 2017) Zor, Kasim; Timur, O?uzhan; Teke, AhmetAccording to privatization and deregulation of power system, accurate electric load forecasting has come into prominence recently. The new energy market and the smart grid paradigm ask for both better demand side management policies and for more reliable forecasts from single end-users, up to system scale. However, it is complex to predict the electric demand owing to the influencing factors such as climate factors, social activities, and seasonal factors. The methods developed for load forecasting are broadly analyzed in two categories, namely analytical techniques and artificial intelligence techniques. In the literature, commonly used analytical methods are linear regression method, Box-Jenkins method, and nonparametric regression method. The analytical methods work well under normal daily circumstances, but they can't give contenting results while dealing with meteorological, sociological or economical changes, hence they are not updated depending on time. Therefore, artificial intelligence techniques have gained importance in reducing estimation errors. Artificial neural network, support vector machine, and adaptive neuro-fuzzy inference system are among these artificial intelligence techniques. In this paper, a state-of-the-art review of three artificial intelligence techniques for short-term electric load forecasting is comprehensively presented. © 2017 IEEE.Öğe A State-of-the-Art Review of Artificial Intelligence Techniques for Short-Term Electric Load Forecasting(IEEE, 2017) Zor, Kasim; Timur, Oguzhan; Teke, AhmetAccording to privatization and deregulation of power system, accurate electric load forecasting has come into prominence recently. The new energy market and the smart grid paradigm ask for both better demand side management policies and for more reliable forecasts from single end-users, up to system scale. However, it is complex to predict the electric demand owing to the influencing factors such as climate factors, social activities, and seasonal factors. The methods developed for load forecasting are broadly analyzed in two categories, namely analytical techniques and artificial intelligence techniques. In the literature, commonly used analytical methods are linear regression method, Box-Jenkins method, and nonparametric regression method. The analytical methods work well under normal daily circumstances, but they can't give contenting results while dealing with meteorological, sociological or economical changes, hence they are not updated depending on time. Therefore, artificial intelligence techniques have gained importance in reducing estimation errors. Artificial neural network, support vector machine, and adaptive neuro-fuzzy inference system are among these artificial intelligence techniques. In this paper, a state-of-the-art review of three artificial intelligence techniques for short-term electric load forecasting is comprehensively presented.Öğe Application of Statistical and Artificial Intelligence Techniques for Medium-Term Electrical Energy Forecasting: A Case Study for a Regional Hospital(Int Centre Sustainable Dev Energy Water & Env Systems-Sdewes, 2020) Timur, Oguzhan; Zor, Kasim; Celik, Ozgur; Teke, Ahmet; Ibrikci, TurgayElectrical energy forecasting is crucial for efficient, reliable, and economic operations of hospitals due to serving 365 days a year, 24/7, and they require round-the-clock energy. An accurate prediction of energy consumption is particularly required for energy management, maintenance scheduling, and future renewable investment planning of large facilities. The main objective of this study is to forecast electrical energy demand by performing and comparing well-known techniques, which are frequently applied to short-term electrical energy forecasting problem in the literature, such as multiple linear regression as a statistical technique and artificial intelligence techniques including artificial neural networks containing multilayer perceptron neural networks and radial basis function networks, and support vector machines through a case study of a regional hospital in the medium-term horizon. In this study, a state-of-the-art literature review of medium-term electrical energy forecasting, data set information, fundamentals of statistical and artificial intelligence techniques, analyses for aforementioned methodologies, and the obtained results are described meticulously. Consequently, support vector machines model with a Gaussian kernel has the best validation performance, and the study revealed that seasonality has a dominant influence on forecasting performance. Hence heating, ventilation, and air-conditioning systems cover the major part of electrical energy consumption of the regional hospital. Besides historical electrical energy consumption, outdoor mean temperature and calendar variables play a significant role in achieving accurate results. Furthermore, the study also unveiled that the number of patients is steady over the years with only small deviations and have no significant influence on medium-term electrical energy forecasting.Öğe Artificial neural network-based discrete-fuzzy logic controlled active power filter(Inst Engineering Technology-Iet, 2014) Saribulut, Lutfu; Teke, Ahmet; Tumay, MehmetArtificial neural network (ANN) is a computational algorithm based on the structure and functions of biological neural networks. It is used for modelling of the non-linear systems that cannot be mathematically expressed by the formula and extraction of the system dynamics, expressed by using the complex mathematical equations, such as harmonics. To show the effective usage of ANNs in the power system, the fundamental harmonic of a load with six-pulse thyristor controlled rectifier is extracted with ANN by using the system variables that are difficult to express with each other. Then, a new approach is proposed to generate the reference signal for compensating the harmonics of the current by using discrete fuzzy logic in this study. In addition, a simple and useful method to determine the circuit parameters of the active power filter (APF) is proposed to reduce the rating of the required filter and the capacitor values without affecting its efficiency. Case studies are performed to test the performance of the proposed control algorithm for APF.Öğe Calculating Payback Periods for Energy Efficiency Improvement Applications at a University Hospital(2015) Teke, Ahmet; Timur, Oğuzhan; Zor, KasımArtan enerji talebi ve mevcut fosil yakıt tabanlı kaynaklarının hızla tükenmesi, ülkelerin enerjiyi verimli kullanmasına, enerji tasarrufu yapmasına ve alternatif enerji kaynaklarını aramasına zorlamıştır. Enerji ihtiyaçlarını karşılamada başka ülkelere bağımlı olan bizim gibi gelişmekte olan ülkelerde, mevcut enerjiyi verimli kullanmak ve tasarruf çalışmaları yapmak daha büyük önem taşımaktadır. Hastaneler binalarda kullanılan toplam enerjinin yaklaşık olarak %6sını tüketmektedir. Isıtma, Havalandırma ve İklimlendirme sistemleri (IHİS), hastanede tüketilen elektrik enerjisinin büyük bir kısmını oluşturmaktadır. İklimlendirme sistemleri toplam tüketilen yaklaşık olarak %70inden sorumludur. Hastanedeki elektrik motorları ve aydınlatma sistemleri yaklaşık olarak toplam enerjinin %19-21 lik kısmını harcamaktadırlar. Bu çalışmada, aydınlatma cihazları, elektrik motorları ve IHİS sistemleri üzerine yapılan detaylı çalışmalar sonucunda, bir üniversite hastanesinde bu cihazların enerji tasarruf potansiyelleri ve geri dönüşüm süreleri tahmin edilmiştir.Öğe Dağıtım sistemleri için çok fonksiyonlu statik senkron kompanzatör(2016) Sarıbulut, Lütfü; Teke, Ahmet; Latran, Mohammad BarghıGüç kalitesi problemleri, elektronik cihazların ve hassas yüklerin zarar görmesine ve verimsiz çalışmalarına neden olmakta ve önemli ölçüde ekonomik kayıplara yol açmaktadır. Bu problemlerin giderilmesi için, sistem dinamiklerindeki değişimlere hızlı cevap vermesi ve güç sistemi parametrelerini kontrol edebilmesi özelliklerinden dolayı Dağıtım Sistemi Statik Senkron Kompanzatör (D-STATKOM) yaygın olarak kullanılmaktadır. Bu araştırma çalışmasında, Genel Dalgacık Dönüşümüne dayalı bir kontrol yöntemine sahip D-STATKOM ilk defa önerilmiş olup, dağıtım sistemlerindeki en önemli güç kalitesi problemlerinden olan dengeli/dengesiz gerilim dalgalanmaları, akım/gerilim harmonikleri ve reaktif güç eş zamanlı olarak kompanze edilmiştir. Bu çalışma ile birlikte genel dalgacık algoritması ilk defa matematiksel olarak formüle edilmiştir. Çeşitli durum çalışmalarıyla önerilen yöntemin performansı PSCAD/EMTDC programı kullanılarak test edilmiştir.Öğe Dağıtım Sistemleri için Çok Fonksiyonlu Statik Senkron Kompanzatörün (D-STATKOM) Tasarımı, Kontrolü ve Modellenmesi(2015) Sarıbulut, Lütfü; Teke, AhmetDünyada ve ülkemizde, elektrik enerjisine olan talebin hızla artması ve gelişen teknoloji ile birlikte elektrik güç sistemlerine bağlı yük karakteristiklerindeki değişimler, elektrikte Güç Kalitesi (GK) konusunun önem kazanmasını sağlamaktadır. Elektrik enerjisi kullanan cihazların hatalı çalışmasına, son kullanıcıların ve yüklerin zarar görmesine sebep olan gerilim veya akımın genlik, frekans ve dalga formundaki değişimler GK problemleri olarak tanımlanmaktadır. Elektrik dağıtım sistemlerinde en sık karşılaşılan elektrik güç kalitesi problemleri gerilim çukuru, gerilim tepeleri, gerilim ve akım harmonikleri, gerilim ve akım dengesizlikleridir. Bu GK problemlerinin etkili bir şekilde kompanze edilmesi, iletim ve dağıtım sistemlerinin düşük maliyetlerle işletilmesine ve tüketicilere daha kaliteli bir elektrik enerjisinin sunulmasına imkân sağlamaktadır. GK problemlerinin düzeltilmesinde geleneksel kompanzasyon cihazları yetersiz kaldığından, ileri teknoloji güç elektroniği tabanlı kompanzasyon cihazlarının geliştirilmesine gereksinim duyulmuştur. Literatürde, reaktif güç akışının kontrolü ve ortak bağlantı noktadaki gerilimin (gerilim çukuru, gerilim tepesinin ve gerilim regülasyonu) düzenlenmesi için Dağıtım Sistemi Statik Senkron Kompanzatörü (D-STATKOM) kullanılmıştır. Harmoniklerin kompanzasyonu için ise Aktif Güç Filtresi (AGF) sistemleri geliştirilmiştir. Son yıllarda akım ve gerilimdeki kalite problemlerinin düzeltilmesi için şebekeye seri/paralel bağlantı cihazlar (Birleştirilmiş Güç Kalitesi Düzenleyici vb.) geliştirilmiştir. Elektrik güç sistemlerinde karşılaşılan farklı güç kalitesi problemlerinin artması ve seri/paralel bağlantılı cihazların maliyetlerinin yüksek olması ya da çok alan kapmalarından dolayı en az güç elektroniği elemanıyla (tek bir evirgeç ile) birçok güç kalitesi probleminin giderilmesi ihtiyacı, son yıllarda D-STATKOM sistemlerinin çok-fonksiyonlu çalışmalarını hızlandırmıştır. D-STATKOM son yıllardaki çalışmalarda, gerilim çukuru, gerilim tepesi, akım ve gerilim harmonikleri, akım ve gerilim dengesizlikleri, gerilim dalgalanması, geçici rejim olaylarının sönümlendirilmesi gibi birçok güç kalitesi probleminin ve reaktif gücün kompanzasyonunda farklı problemleri kompanze edebilecek şekilde geliştirilmiş bir sistemdir. D-STATKOM’daki son gelişmeler birden fazla güç kalitesi problemini eş zamanlı olarak düzeltebilmektedir veya problemin önemine göre çalışma modunu değiştirerek (örneğin D-STATKOM modundan AGF moduna geçiş yapılması) bu problemlerin bazılarını kompanze etmektedir. Son yıllarda literatürde D-STATKOM ile ilgili çalışmaların sayısı artmıştır ve bu çalışmalarda birçok farklı amaç için faklı D-STATKOM topolojileri ve kontrolcüleri önerilmiştir. Bu projede, dağıtım sistemlerinde sıklıkla görülen dengeli/dengesiz gerilim çukuru/tepeleri, harmonikler, gerilim regülasyonu, geçici rejim, faz açısındaki atlamalar ve reaktif güç kompanzasyonu önerilecek D-STATKOM ve kontrol yöntemiyle eş zamanlı olarak kompanze edilebilecektir. Bu işlevlerin gerçekleştirilmesinde gerekli olan kontrol sinyallerinin çıkartılması için Dalgacık Dönüşümü tabanlı kontrol algoritması geliştirilecek ve evirgeçte bulunan DA kondansatörün gerilim regülasyonu için Bulanık Mantık tabanlı kontrolcü tasarlanacaktır. Çok-fonksiyonlu olarak geliştirilecek olan D-STATKOM’un işlevi ilk defa detaylı olarak incelenecektir. Proje kapsamında önerilen D-STATKOM, 5-kVA gücünde, 380-V 3-faz 3-telli elektrik güç sisteminde tasarlanacak ve PSCAD/EMTDC benzetim ortamında test edilecektir. Projenin başarıyla tamamlanması durumunda tek bir evirgeç kullanılarak en az maliyetle (tek evirgeç kullanarak) birçok GK probleminin eş zamanlı olarak kompanze edilmesi sağlanacaktır. Bu incelemeler sonucunda yapılacak yayınlar, bilimsel literatüre ve ülkemizin isminin duyulmasına katkı sağlayacaktır. Ayrıca, ülke çapında yeni bir atılım olan “Elektrik güç kalitesi problemlerine çözüm arama” konusundaki çalışmaların ülkemizde de gerçekleştirilmesine öncülük edilmiş olacaktır. Proje kapsamındaki çalışmaların 12 ay içerisinde tamamlanması hedeflenmektedir.Öğe ENHANCEMENT OF A LOW-COST INTELLIGENT DEVICE FOR IMPROVING ENERGY EFFICIENCY IN BUILDINGS(2018) Timur, Oğuzhan; Teke, Ahmet; Zor, Kasım; Çelik, ÖzgürAscending energy demand and vanishing nonrenewable energyresources have increased the prominence of energy efficiency nowadays. In orderto accomplish an energy efficient future for the forthcoming generations ofhumanity, traditional devices possessing low efficiency should be altered withintelligent devices having high efficiency. Thanks to internet of things (IoT),conventional devices have been turned into intelligent devices which can beremotely accessed, monitored and controlled by utilizing ubiquitous sensormechanisms. In this study, enhancement of a low-cost intelligent device (iDev) istargeted to develop in order to compute and store electrical energy consumptioninto a database by an embedded card named as Arduino which has the capabilityof measuring voltage, current, frequency and power factor for improving energyefficiency in buildings. When a classic device is connected to the enhanced iDev,the device is converted to an intelligent device that is remotely controllable. As aconsequence, if a whole building is equipped with the proposed iDev, it isconsidered that a large amount of electrical energy will be saved for a better andlivable Earth.Öğe Estimating daily Global solar radiation with graphical user interface in Eastern Mediterranean region of Turkey(Pergamon-Elsevier Science Ltd, 2018) Yildirim, H. Basak; Celik, Ozgur; Teke, Ahmet; Barutcu, BurakIt is important to have accurate knowledge on global solar radiation for optimum design of solar energy conversion systems. However, global solar radiation measurement is very rare in meteorological stations in all around the world. Hence, modeling global solar radiation is an crucial issue to fill the gaps in database and to estimate global solar radiation in places where global solar radiation measurement is not available. This paper presents a detailed description and analysis of various global solar radiation modeling methods. The efficiency and accuracy of ten models from different functions to estimate daily solar radiation in EMR are investigated. Also an optimized model based on Artificial Neural Network (ANN) method and Angstrom-Prescott model for the estimation of daily global solar radiation are presented. The essence of this paper is to investigate the performance of the ANN model and Angstrom-Prescott model in order to ensure the most feasible solution for estimating daily global solar radiation for Eastern Mediterranean Region (EMR) of Turkey. 11 years solar radiation data from 4 stations are utilized in training and testing of developed ANN model and parametric model which is based on Angstrm-Prescott method. In order to ensure a simple application of the model that most accurately predicts the desired target value, a new graphical user interface is developed with MATLAB GUI.Öğe Estimating the monthly global solar radiation for Eastern Mediterranean Region(Pergamon-Elsevier Science Ltd, 2014) Teke, Ahmet; Yildirim, H. BasakSolar energy has an important role to achieve the goal of replacing fossil fuels and significant potential to reduce greenhouse gas emissions. Accurate information on solar radiation is very essential for engineers, architects and agriculturist to design the energy systems based on the solar source. The sunshine duration and air temperature are measured by most of the meteorological services in all over the world but global solar radiation measurements are very rare and some of the data are missing. At this point, estimation of solar radiation where stations are not available plays an important role. Different models have been developed in the literature to estimate solar radiation. Angstrom-Prescott sunshine based model is widely used one and also there are some other approaches based on Angstrom model in the literature. In this study, linear, quadratic and cubic empirical as a general equation for throughout the year are generated to estimate global solar radiation in Eastern Mediterranean Region (EMR) which covers the four main cities (Adana, Mersin, Antakya and Kahramanmaras) by using the meteorological data in the Turkish State Meteorological Services. Regression models were estimated for each month separately and annually by curve estimation techniques with MINITAB statistical program. The monthly linear, quadratic and cubic models for estimating monthly average global solar radiation are validated as well. Finally, a comparison between monthly models and general models is performed by statistical test methods such as R-2, MPE and MAPE. According to statistical test results, the use of cubic general model for EMR is recommended. (C) 2014 Elsevier Ltd. All rights reserved.Öğe Evaluation and performance comparison of different models for the estimation of solar radiation(Pergamon-Elsevier Science Ltd, 2015) Teke, Ahmet; Yildirim, H. Basak; Celik, OzgurThe rapid depletion of energy resources, increasing energy demand and degeneration of ecological values need an urgent solution in this age. Solar energy as the most important energy resource has become part of the solution to the world's energy challenges. Solar radiation data that provides the information on how much energy strikes to the earth is needed for utilization, planning and designing of solar power plants. The measurement of solar radiation data is generally available in some specific areas due to difficulty in solar radiation measurements in terms of its initial and maintenance costs. Therefore, solar energy modelling techniques are becoming more and more important due to the increasing need for the design, performance evaluation and improvement of the solar energy applications. The primary aim of this paper is to overview solar radiation modelling techniques to identify optimum models available and to classify research fields in the literature. In this paper, the modelling techniques, data information, accuracy tests of models used in around 90 papers were reviewed and the most accurate models were suggested. (C) 2015 Elsevier Ltd. All rights reserved.Öğe Evaluation of artificial neural network methods to forecast short-term solar power generation: a case study in Eastern Mediterranean Region(Tubitak Scientific & Technological Research Council Turkey, 2022) Bozkurt, Helin; Macit, Ramazan; Celik, Ozgur; Teke, AhmetSolar power forecasting is substantial for the utilization, planning, and designing of solar power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role in solar power generation. The ever-changing meteorological variables and imprecise measurement of GSI raise difficulties for forecasting photovoltaic (PV) output power. In this context, a major motivation appears for the accurate forecast of GSI to perform effective forecasting of the short-term output power of a PV plant. The presented study comprises of four artificial neural network (ANN) methods; recurrent neural network (RNN) method, feedforward backpropagation neural network (FFBPNN) method, support vector regression (SVR) method, and long short-term memory (LSTM) for daily total GSI prediction of Tarsus by using meteorological data. Moreover, this study proposes a model that utilizes the predicted daily GSI for output power forecasting of a grid-connected PV plant. The obtained results are compared with the output power generation data of a 350 kW solar power plant. The results are evaluated with the performance indices as mean absolute percentage error (MAPE), normalized root mean squared error (NRMSE), weighted mean absolute error (WMAE), and normalized mean absolute error (NMAE). FFBPNN method is chosen with the best results of MAPE 7.066%, NMAE 3.629%, NRMSE 4.673%, and WMAE 5.256%.Öğe Evaluation of classical parametric models for estimating solar radiation in the Eastern Mediterranean region of Turkey(Pergamon-Elsevier Science Ltd, 2018) Yildirim, H. Basak; Teke, Ahmet; Antonanzas-Torres, FernandoAccurate information on global solar radiation is essential to design and operate the systems that are based on solar energy. However, global solar radiation measurement is very rare while the measurements of other meteorological parameters such as air temperature, relative humidity, sunshine duration and precipitation are common in meteorological stations all around the world. Therefore, modelling global solar radiation is an important issue to fill the gaps in database and to estimate global solar radiation in places where global solar radiation measurement is not available. There are many different approaches in the literature for modelling global solar radiation. Two new methodologies are presented in this paper to develop parametric models for estimation of daily global solar radiation based on sunshine duration and relative humidity as well as a review of fourteen different already exist parametric models which are based on air temperature, maximum temperature, minimum temperature, precipitation, sunshine duration and relative humidity. The proposed models improve the estimation results of the other fourteen models with average mean absolute error (MAE) of 0.947 MJ/m(2) for Adana station, 1.086 MJ/m(2) for Goksun station, 1.074 MJ/m(2) for Tarsus station and 1.060 MJ/m(2) for whole study area. Hence, the proposed models which significantly approximate to measurements from pyranometers can be useful for the modelling global solar radiation in Eastern Mediterranean Region.Öğe Improvement of energy harvesting capability in grid-connected photovoltaic micro-inverters(Taylor & Francis Inc, 2024) Celik, Ozgur; Tan, Adnan; Inci, Mustafa; Teke, AhmetIn this paper, a multi-stage micro-inverter system depending on a dual neutral point clamped (D-NPC) inverter is developed for low power photovoltaic (PV) applications. The primary objectives of this study are determined as improving the performance of the proposed D-NPC inverter-based micro-inverter and its controller to ensure better system reliability and promote the overall efficiency. In comparison with conventional systems, the designed system provides many advantages: (1) utilization of lower rating switching components, (2) reduced switching frequency and voltage stress, (3) reduced the size of filter components, (4) higher efficiency, and (5) lower total harmonic distortion (THD). The weighted efficiency of the system is remarkably increased by the value of 2.15% compared to the conventional micro-inverter and reached 93.73%. Furthermore, the THD value of the output current is measured below 3% for the proposed system with the small size of passive filtering elements. Further, dynamic grid support and anti-islanding detection capabilities are provided through the utilized controller structure to fulfill grid codes. Consequently, obtained results demonstrate that the improved system can be a considerable candidate for photovoltaic micro-inverter applications in terms of weighted efficiency, output current THD, and reasonable cost.Öğe Multi-functional static synchronous compensator for distribution systems(Gazi Univ, Fac Engineering Architecture, 2016) Saribulut, Lutfu; Teke, Ahmet; Latran, Mohammad BarghiPower quality problems cause the damage and inefficient operation of the electronic devices and sensitive loads, and lead to substantial economic losses. In order to eliminate these problems, Distribution Static Synchronous Compensator (D-STATCOM) is widely used due to the fast response to the changes in the system dynamics and the capability of controlling the power system parameters. In this research study, D-STATCOM, having a control method based on General Wavelet Transform, is firstly introduced and the balanced/unbalanced voltage fluctuations, current/voltage harmonics and reactive power that are most common types of power quality problems in the distribution systems are simultaneously compensated. With this study, it is the first time to formulate the general wavelet algorithm as mathematically in the literature. The performance of the proposed method is tested with several case studies by using PSCAD/EMTDC program.Öğe Overview of micro-inverters as a challenging technology in photovoltaic applications(Pergamon-Elsevier Science Ltd, 2018) Celik, Ozgur; Teke, Ahmet; Tan, AdnanOne of the key components of the photovoltaic (PV) system is inverters due to their function as being an operative interface between PV and the utility grid or residential application. In addition, they can be employed as power quality conditioners at the point of common coupling (PCC). It should be noted that in inverter technologies, there has been an increasing interest to achieve robust output power injection capabilities with lesser design complexity in terms of controller part and power circuit topology. Micro-inverters (MIs) are module based type of inverters that have aroused much interest in recent years. Owing to their distributed architecture mounted with individual PV modules, system reliability can be improved remarkably by using MIs. Furthermore, a module based nature of the MI architecture provides a number of advantages, such as low converter power rating, low power losses, accurate maximum power point tracking (MPPT) ability against partially shading conditions and elimination of PV panel mismatches. However, there is still known weighted conversion efficiency of MIs ranges between 90% and 95%. Therefore, novel designs focus on the known weak aspects of traditional MIs and their failure mechanisms. In this paper, state-of-the-art technologies for MIs with a detailed survey on the technical features consisting of power circuit configuration, control structures, grid compatibility abilities, decoupling capacitor placement, energy harvesting capabilities, and safety mechanisms are presented. Additionally, elaborated comparison on MIs topologies is realized and some future research fields on MIs are summarized.Öğe Power Quality Enhancement in Hybrid PV-BES System based on ANN-MPPT(Tubitak Scientific & Technological Research Council Turkey, 2024) Bozkurt, Helin; Celik, Ozgur; Teke, AhmetBattery energy systems (BESs) assisted photovoltaic (PV) plants are among the popular hybrid power systems in terms of energy efficiency, energy management, uninterrupted power supply, grid-connected and off-grid availability. The primary objective of this study is to enhance the power quality of a grid-tied PV-BES hybrid system by developing an operational strategy based on artificial neural network (ANN) based maximum power point tracking (MPPT) method. A test system comprising a 10-kWh BES and a 12.4 kW PV plant is structured and simulated on the MATLAB/Simulink platform. The hybrid system is validated with three different cases: constant radiation, rapid changing radiation, and real-day solar radiation data from the Turkish State Meteorological Service of Tarsus (Mersin, Turkiye) employing the developed operational strategy. These cases involve the examination of three distinct MPPT methods, analyzing DC-link voltage, battery state of charge (SOC), current, voltage, and system total harmonic distortion (THD). The simulation results indicate that the developed operational strategy with the ANN-MPPT method yields superior THD results in output current and a more stable DC-link voltage. Furthermore, the strategy shows improved convergence speed and reduced oscillations to achieve diverse reference operating points under varying atmospheric conditions compared to conventional MPPT methods. Numerical results demonstrate that the developed operational strategy with the ANN-MPPT consistently maintains THD values below 3% and exhibits a stable DC-link voltage deviation of 1.42% in various charging modes for both rapidly changing radiation and real-day solar radiation data.
