Yapay sinir ağları kullanılarak fotovoltaik sistemin maksimum güç noktası takibi

Year 2023, Volume: 13 Issue: 3, 733 - 749, 15.07.2023

Leyla Karagözoğlu , Zeynep Bala Duranay

https://doi.org/10.17714/gumusfenbil.1217821

Abstract

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.

Keywords

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

Thanks

Bu makale Leyla Karagözoğlu’nun yüksek lisans tez çalışmasından üretilmiştir.

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

Abstract

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

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