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

Leyla Karagözoğlu; Zeynep Bala Duranay

Makale detayları

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

Yazarlar:

Leyla Karagözoğlu | Zeynep Bala Duranay

Yayıncı:

Gumushane University

Ana Başlıklar:

Mühendislik

Dergi:

Gümüşhane Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.733-749

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001295850

pISSN:

2146-538X

Anahtar kelimeler:

Özet:

 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 MATLABSimulink 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 1000Wm2 to 200Wm2 at regular intervals. When temperature was increased from 25C to 45C 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 1000Wm2 and temperature 25C, 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.

 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 MATLABSimulink 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 1000Wm2 to 200Wm2 at regular intervals. When temperature was increased from 25C to 45C 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 1000Wm2 and temperature 25C, 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.

 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 MATLABSimulink 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 1000Wm2 to 200Wm2 at regular intervals. When temperature was increased from 25C to 45C 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 1000Wm2 and temperature 25C, 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.

 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 MATLABSimulink ortamında gerçekleştirilmiştir. Levenberg-Marquardt algoritmasının kullanıldığı ağın eğitiminde, 70i eğitim, 15i geçerlilik ve 15i test aşamalarında olmak üzere toplam 1000 veri kullanılmıştır. Işınımın 1000Wm2den 200Wm2ye belirli aralıklarla azaltıldığı sistemde, fotovoltaik panelin gücünün 225.1Wdan 46.9Wa, yükseltici konvertör gücünün 220.9Wdan 45.75Wa kadar azaldığı izlenmiştir. Sıcaklığın 25Cden 45Cye belirli aralıklarla arttırıldığı sistemde, fotovoltaik panel gücünün 225.1Wdan 194.6Wa, yükseltici konvertörün gücünün 220.9Wdan 190.7Wa kadar azaldığı görülmüştür. Sabit ışınım 1000Wm2 ve sıcaklık 25C 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.

Makale detayları

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

Yazarlar:

Leyla Karagözoğlu | Zeynep Bala Duranay

Yayıncı:

Gumushane University

Ana Başlıklar:

Mühendislik

Dergi:

Gümüşhane Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.733-749

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001295850

pISSN:

2146-538X

Photovoltaic

Solar energy

Maximum power point

Artificial neural network

Renewable energyFotovoltaik

Güneş enerjisi

Maksimum güç noktası

Yapay sinir ağı

Yenilenebilir enerji

Sayı

Makale detayları

Yazarlar: Leyla Karagözoğlu | Zeynep Bala Duranay

Yayıncı: Gumushane University

Ana Başlıklar: Mühendislik

Dergi: Gümüşhane Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.733-749

Belge Tipi: Makale

Yayın Tarihi: 7/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001295850

pISSN: 2146-538X

Sayı

Yazarlar:

Leyla Karagözoğlu | Zeynep Bala Duranay

Yayıncı:

Gumushane University

Ana Başlıklar:

Mühendislik

Dergi:

Gümüşhane Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.733-749

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001295850

pISSN:

2146-538X