Yapay sinir ağları kullanılarak fotovoltaik sistemin maksimum güç noktası takibi

Bu çalışmada, bir fotovoltaik sistemin yapay sinir ağı kullanılarak maksimum güç noktası takibinin benzetimi yapılmıştır. Fotovoltaik sistemlere olan ilgi, fosil kaynakların yetersizliği ve enerjiye olan talebin yükselmesi ile giderek artmaktadır. Fotovoltaik sistem performansının çevre koşullarına göre değişmesi, sistem verimliliğini düşürmektedir. Bunun önüne geçmek, maksimum güç noktasına ulaşmakla mümkündür. Sistemi maksimum güç noktasında çalıştırmaya yönelik birçok teknik geliştirilmiştir. Yapay zekanın yaygınlaşmasıyla, maksimum güç noktası tespitinde akıllı sistemler kullanılmaya başlanmıştır. Akıllı sistemlerden biri olan yapay sinir ağı, öğrenebilme, genelleme yapabilme ve karar verme kabiliyetine sahiptir. Bu çalışmada giriş değişkenleri sıcaklık ve ışınım olan bir yapay sinir ağı ile maksimum güç noktası tespit edilmektedir. Sistemin benzetimi MATLAB/Simulink ortamında gerçekleştirilmiştir. Levenberg-Marquardt algoritmasının kullanıldığı ağın eğitiminde, %70’i eğitim, %15’i geçerlilik ve %15’i test aşamalarında olmak üzere toplam 1000 veri kullanılmıştır. Işınımın 1000W/m2’den 200W/m2’ye belirli aralıklarla azaltıldığı sistemde, fotovoltaik panelin gücünün 225.1W’dan 46.9W’a, yükseltici konvertör gücünün 220.9W’dan 45.75W’a kadar azaldığı izlenmiştir. Sıcaklığın 25°C’den 45°C’ye belirli aralıklarla arttırıldığı sistemde, fotovoltaik panel gücünün 225.1W’dan 194.6W’a, yükseltici konvertörün gücünün 220.9W’dan 190.7W’a kadar azaldığı görülmüştür. Sabit ışınım (1000W/m2) ve sıcaklık (25°C) değerlerinde, sistem %98 ile en yüksek verim değerine sahiptir. Sabit sıcaklık ve farklı ışınım koşullarında, sistem verimi incelenmiş, ışınım değeri azaldıkça sistem veriminin azaldığı görülmüştür. Benzer olarak, sabit ışınım ve farklı sıcaklık koşulları altında, sıcaklık artışının sistem veriminin azalmasına neden olduğu belirlenmiştir. Sonuçlar, yapay sinir ağı tabanlı maksimum güç noktası izleme sistemine sahip fotovoltaik sistemin, değişen çevre koşullarında maksimum güç noktasını izlediğini göstermektedir.

Anahtar Kelimeler:

Fotovoltaik, Güneş enerjisi, Maksimum güç noktası, Yapay sinir ağı, Yenilenebilir enerji

Maximum power point tracking of the photovoltaic system using artificial neural networks

In this study, a photovoltaic system is simulated for maximum power point tracking using an artificial neural network. The interest in photovoltaic systems is increasing with inadequacy of fossil resources and rise in demand for energy. The variation of photovoltaic system performance depending on environmental conditions reduces efficiency. It is possible to prevent this by reaching maximum power point. Many techniques have been developed to operate system at maximum power point. With the spread of artificial intelligence, smart systems have started to be used in determining maximum power point. Artificial neural networks are intelligent systems that have the ability to learn, generalize and make decisions. In this study, maximum power point is determined with an artificial neural network whose inputs are temperature and radiation. The system was simulated in MATLAB/Simulink environment. A total of 1000 data were used in training of network in which Levenberg-Marquardt algorithm was used, 70% in training, 15% in validation and 15% in testing stages. It was observed that power of photovoltaic panel decreased from 225.1W to 46.9W, and power of boost converter from 220.9W to 45.75W when radiation was reduced from 1000W/m2 to 200W/m2 at regular intervals. When temperature was increased from 25°C to 45°C at regular intervals, it was determined that power of photovoltaic panel decreases from 225.1W to 194.6W, and power of boost converter from 220.9W to 190.7W. At constant radiation (1000W/m2) and temperature (25°C), system has the highest efficiency value of 98%. At constant temperature and different radiation conditions, it was seen that efficiency decreased as radiation value decreased. Similarly, under constant radiation and different temperature conditions, temperature increase caused a decrease in efficiency. The results show that photovoltaic system with artificial neural network based maximum power point tracking reaches maximum power point under changing environmental conditions.

Keywords:

Photovoltaic, Solar energy, Maximum power point, Artificial neural network, Renewable energy,

PDF

___

Berrera, M., Dolara, A., & Leva, S. (2009). Experimental test of seven widely adopted MPPT algorithms. 2009 Bucharest PowerTech Conference (ss. 1-8). Bucharest, Romania: IEEE. https://doi.org/10.1109/PTC.2009.5282010
Bouakkaz, M. S., Boukadoum, A., Boudebbouz, O., Attoui, I., Boutasseta, N., & Bouraiou, A. (2020). Fuzzy logic based adaptive step hill climbing MPPT algorithm for PV energy generation systems. 2020 International Conference on Computing and Information Technology (ICCIT-1441) (ss. 1-5). Tabuk, Saudi Arabia: IEEE. https://doi.org/10.1109/ICCIT-144147971.2020.9213737
Chy, D. K., & Khaliluzzaman, M. (2015). Experimental assessment of PV arrays connected to buck-boost converter using MPPT and Non-MPPT technique by implementing in real time hardware. 2015 International Conference on Advances in Electrical Engineering (ICAEE) (ss. 306-309). Dhaka, Bangladesh: IEEE. https://doi.org/10.1109/ICAEE.2015.7506856
Çoruh, N., Erfidan, T., & Ürgün, S. (2008). DA-DA Boost Dönüştürücü Tasarımı ve Gerçeklenmesi, Elektrik- Elektronik-Bilgisayar Mühendisliği Sempozyumu (ELECO) (pp. 362-365), Bursa.
Dadfar, S., Wakil, K., Khaksar, M., Rezvani, A., Miveh, M. R., & Gandomkar, M. (2019). Enhanced control strategies for a hybrid battery/photovoltaic system using FGS-PID in grid-connected mode. International Journal of Hydrogen Energy, 44(29), 14642-14660. https://doi.org/10.1016/j.ijhydene.2019.04.174
Divyasharon, R., Narmatha Banu, R., & Devaraj, D. (2019). Artificial neural network based MPPT with cuk converter topology for PV systems under varying climatic conditions. 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) (ss. 1-6). Tamilnadu, India: IEEE. https://doi.org/10.1109/INCOS45849.2019.8951321
Duranay, M., Turmus, A., & Tanyildizi, V. (2021). Experimental efficiency analysis of a solar panel electricity generation system using planar reflection. IET Renewable Power Generation, 15(3), 521-531. https://doi.org/10.1049/rpg2.12012
Duranay, Z. B., & Guldemir, H. (2019). Modelling and simulation of a single phase standalone PV system. 2019 International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (ss. 1-6). Pitesti, Romania: IEEE. https://doi.org/10.1109/ECAI46879.2019.9041997
Duranay, Z. B., & Guldemir, H. (2020). Voltage controlled boost converter-inverter system for photovoltaic applications. Turkish Journal of Science and Technology, 15(2), 85-92.
Duranay, Z. B., & Guldemir, H. (2021). Fuzzy logic based maximum power point tracking for photovoltaic systems. 2021 International Conference of Society for Electronics, Telecommunications, Automatics and Informatics of the Republic of North Macedonia (ETAI) (ss. 95-101), Macedonia.
Elaissaoui, H., Zerouali, M., Ougli, A. E., & Tidhaf, B. (2020). MPPT algorithm based on fuzzy logic and artificial neural network (ANN) for a hybrid solar/wind power generation system. 2020 International Conference On Intelligent Computing in Data Sciences (ICDS) (ss. 1-6). Fez, Morocco: IEEE. https://doi.org/10.1109/ICDS50568.2020.9268747
Fathi, M., & Parian, J. A. (2021). Intelligent MPPT for photovoltaic panels using a novel fuzzy logic and artificial neural networks based on evolutionary algorithms. Energy Reports, 7, 1338-1348. https://doi.org/10.1016/j.egyr.2021.02.051
Gani, A., Açıkgöz, H., & Şekkeli, M. (2020). Fotovoltaik sistemlerde değişken yük ve güneş ışınımı altında sinirsel-bulanık denetleyici ile maksimum güç noktası takibi. Avrupa Bilim ve Teknoloji Dergisi, 19, 734-745.
Gani, A. (2021). Imroving dynamic efficiency of photovoltaic generation systems using adaptive type 2 fuzzy- neurol network via EN 50530 test procedure. International Journal of Circuit Theory and Applications, 49(11), 3922-3940.
Gündoğdu, A., & Çelikel, R. (2020). ANN-based MPPT algorithm for photovoltaic sytems. Turkish Journal of Science & Technology, 15(2), 101-110.
Hart, G. W., Branz, H. M., & Cox Iii, C. H. (1984). Experimental tests of open-loop maximum-power-point tracking techniques for photovoltaic arrays. Solar Cells, 13(2), 185-195. https://doi.org/10.1016/0379-6787(84)90008-5
Hasson, U., Nastase, S. A., & Goldstein, A. (2020). Direct fit to nature: An evolutionary perspective on biological and artificial neural networks. Neuron, 105(3), 416-434. https://doi.org/10.1016/j.neuron.2019.12.002
Hsiao, Y. T., & Chen, C. H. (2002). Maximum power tracking for photovoltaic power system. 2002 IEEE Industry Applications Conference (ss. 1035-1040). Pittsburgh, PA, USA: IEEE. https://doi.org/10.1109/IAS.2002.1042685
Ibrahim, A., Jin, X., Dai, X., Sarhan, M. A., Shafik, M. B., & Zhou, H. (2019). Artificial neurol network based maximum power point tracking for PV sytems. Proceedings of the 38th Chinese Control Conference (ss. 6559-6564), Guangzhou, China.
Joshi, P., & Arora, S. (2017). Maximum power point tracking methodologies for solar PV systems–a review. Renewable and Sustainable Energy Reviews, 70, 1154-1177. https://doi.org/10.1016/j.rser.2016.12.019
Jyothy Lakshmi, P. N., & Sindhu M. R. (2018). An artificial neural network based MPPT algorithm for solar PV sytem. 4th International Conference on Electrical Energy Sytems (ICEES) (ss. 375-380), Chennai, India.
Karagözoğlu, L., & Duranay, Z. B. (2021). Fotovoltaik panel performansını etkileyen faktörlerin incelenmesi. 2021 2nd International Baku Conference on Scientific Research (ss. 218-228), Baku.
Kobayashi, K., Matsuo, H., & Sekine, Y. (2004). A novel optimum operating point tracker of the solar cell power supply system. 2004 IEEE Power Electronics Specialists Conference (ss. 2147-2151). Aachen, Germany: IEEE. https://doi.org/10.1109/PESC.2004.1355451
Köse, E. (2018). Fotovoltaik sistemlerin maksimum güç noktasında çalıştırılması. Dünya Multidisipliner Araştırmalar Dergisi, 2018(1), 8-27.
Kurak, E., Erdemir, V., & Dursun, B. (2016). PV sistemin için maksimum güç noktası izleyicisi tasarım ve uygulanması. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 4(2), 581-592.
Liu, F., Duan, S., Liu, F., Liu, B., & Kang, Y. (2008). A variable step size INC MPPT method for PV systems. IEEE Transactions on Industrial Electronics, 55(7), 2622-2628. https://doi.org/10.1109/TIE.2008.920550.
Liu, Y., Li, M., Ji, X., Luo, X., Wang, M., & Zhang, Y. (2014). A comparative study of the maximum power point tracking methods for PV systems. Energy Conversion and Management, 85, 809–816. https://doi.org/10.1016/j.enconman.2014.01.049
Mao, M., Cui, L., Zhang, Q., Guo, K., Zhou, L., & Huang, H. (2020). Classification and summarization of solar photovoltaic MPPT techniques: A review based on traditional and intelligent control strategies. Energy Reports, 6, 1312-1327.
Makhloufi, M. T., Khireddine, M. S., Abdessemed, Y., & Boutarfa, A. (2014). Tracking power photovoltaic sytem using artificial neural network control strategy. I.J. Intelligent Sytems and Applications, 6(12), 17-26.
Öztemel, E. (2003). Yapay sinir ağları. Papatya yayıncılık.
Safari, A., & Mekhilef, S. (2010). Simulation and hardware implementation of incremental conductance MPPT with direct control method using cuk converter. IEEE Transactions on Industrial Electronics, 58(4), 1154–1161. https://doi.org/10.1109/TIE.2010.2048834
Seyedmahmoudian, M., Horan, B., Soon, T. K., Rahmani, R., Oo, A. M. T., Mekhilef, S., & Stojcevski, A. (2016). State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems–a review. Renewable and Sustainable Energy Reviews, 64, 435–455. https://doi.org/10.1016/j.rser.2016.06.053
Sreekanth, S., & Raglend, I. J. (2012). A comparitive and analytical study of various incremental algorithms applied in solar cell. 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET) (ss. 452-456). Nagercoil, India: IEEE. https://doi.org/10.1109/ICCEET.2012.6203876
Wasynezuk, O. (1983). Dynamic behavior of a class of photovoltaic power systems. IEEE Transactions on Power Apparatus and Systems, 9, 3031-3037. https://doi.org/10.1109/TPAS.1983.318109