Performance Evaluation of a Nature-Inspired Three-Particle Swarm Optimization Algorithm in PV Systems with a Modified SEPIC Converter

Muhammed Mahho; Mehmet Yılmaz; Muhammedfatih Corapsiz

Makale detayları

Performance Evaluation of a Nature-Inspired Three-Particle Swarm Optimization Algorithm in PV Systems with a Modified SEPIC Converter

Yazarlar:

Muhammed Mahho | Mehmet Yılmaz | Muhammedfatih Corapsiz

Yayıncı:

Bingol University

Ana Başlıklar:

Fotovoltaik Güç Sistemleri

Dergi:

Türk Doğa ve Fen Dergisi Vol.14 Issue 3, pp.57-66

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359151

pISSN:

2149-6366

Anahtar kelimeler:

Özet:

 The increase in world population, the development of industrialization and peoples use of more advanced technologies increase energy demands day by day. People have started to turn to alternative energy sources to avoid the energy crisis that will occur when existing energy sources are exhausted. Photovoltaic PV systems are an advantageous option among sustainable energy sources. PV systems are affected by irradiance intensity and temperature. To overcome this problem, Maximum power point tracking MPPT algorithms are used. In this study, in order to maximize the efficiency of PV systems, the performance of Particle swarm optimization PSO, Deterministic Particle swarm optimization DPSO, Enhance Autonomous Group Particle swarm optimization EAGPSO algorithms were evaluated by using a high gain modified SEPIC converter. PSO, DPSO and EAGPSO algorithms were evaluated for three different scenarios under normal irradiance and partial shading conditions. It has been observed that the EAGPSO algorithm has the highest MPPT efficiency of 98.9 and a convergence time of 0.37s for different scenarios. In addition, it has been found that the power oscillation of the EAGPSO algorithm is reduced by approximately half compared to DPSO and by approximately two thirds compared to PSO.

 The increase in world population, the development of industrialization and peoples use of more advanced technologies increase energy demands day by day. People have started to turn to alternative energy sources to avoid the energy crisis that will occur when existing energy sources are exhausted. Photovoltaic PV systems are an advantageous option among sustainable energy sources. PV systems are affected by irradiance intensity and temperature. To overcome this problem, Maximum power point tracking MPPT algorithms are used. In this study, in order to maximize the efficiency of PV systems, the performance of Particle swarm optimization PSO, Deterministic Particle swarm optimization DPSO, Enhance Autonomous Group Particle swarm optimization EAGPSO algorithms were evaluated by using a high gain modified SEPIC converter. PSO, DPSO and EAGPSO algorithms were evaluated for three different scenarios under normal irradiance and partial shading conditions. It has been observed that the EAGPSO algorithm has the highest MPPT efficiency of 98.9 and a convergence time of 0.37s for different scenarios. In addition, it has been found that the power oscillation of the EAGPSO algorithm is reduced by approximately half compared to DPSO and by approximately two thirds compared to PSO.

 The increase in world population, the development of industrialization and peoples use of more advanced technologies increase energy demands day by day. People have started to turn to alternative energy sources to avoid the energy crisis that will occur when existing energy sources are exhausted. Photovoltaic PV systems are an advantageous option among sustainable energy sources. PV systems are affected by irradiance intensity and temperature. To overcome this problem, Maximum power point tracking MPPT algorithms are used. In this study, in order to maximize the efficiency of PV systems, the performance of Particle swarm optimization PSO, Deterministic Particle swarm optimization DPSO, Enhance Autonomous Group Particle swarm optimization EAGPSO algorithms were evaluated by using a high gain modified SEPIC converter. PSO, DPSO and EAGPSO algorithms were evaluated for three different scenarios under normal irradiance and partial shading conditions. It has been observed that the EAGPSO algorithm has the highest MPPT efficiency of 98.9 and a convergence time of 0.37s for different scenarios. In addition, it has been found that the power oscillation of the EAGPSO algorithm is reduced by approximately half compared to DPSO and by approximately two thirds compared to PSO.

 Dünya nüfusunun artması, sanayileşmenin gelişmesi ve insanların daha ileri teknolojileri kullanması enerji taleplerini her geçen gün artırmaktadır. İnsanlar mevcut enerji kaynakları tükendiğinde ortaya çıkacak enerji krizinden kaçınmak için alternatif enerji kaynaklarına yönelmeye başlamıştır. Fotovoltaik PV sistemler sürdürülebilir enerji kaynakları arasında avantajlı bir seçenektir. PV sistemler ışınım şiddetinden ve sıcaklıktan etkilenmektedir. Bu sorunu aşmak için Maksimum güç noktası izleme MPPT tabanlı algoritmalar kullanılır. Bu çalışmada PV sistemlerin verimliliğini en üst düzeye çıkarmak için, yüksek kazançlı modifiye edilmiş SEPIC dönüştürücü kullanılarak Parçacık sürü optimizasyon PSO, Deterministik Parçacık sürü optimizasyon DPSO, Geliştirilmiş Otonom Grup Parçacık Sürü Optimizasyonu EAGPSO algoritmalarının performansı değerlendirilmiştir. PSO, DPSO ve EAGPSO algoritmaları normal ışınım ve kısmi gölgelenme koşulları altında üç farklı senaryo için değerlendirilmiştir. EAGPSO algoritması farklı durumlar için en yüksek 98.9 MPPT verimliliğine ve 0.37s yakınsama süresine sahip olduğu gözlemlenmiştir. Ayrıca EAGPSO algoritmasının güç salınımının DPSOya göre yaklaşık yarı yarıya, PSOya göre ise yaklaşık üçte iki oranında azaldığı elde edilmiştir.

Makale detayları

Performance Evaluation of a Nature-Inspired Three-Particle Swarm Optimization Algorithm in PV Systems with a Modified SEPIC Converter

Yazarlar:

Muhammed Mahho | Mehmet Yılmaz | Muhammedfatih Corapsiz

Yayıncı:

Bingol University

Ana Başlıklar:

Fotovoltaik Güç Sistemleri

Dergi:

Türk Doğa ve Fen Dergisi Vol.14 Issue 3, pp.57-66

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359151

pISSN:

2149-6366

Maximum power point tracking

Modified Sepic Converter

Deterministic Particle swarm optimization

Enhance Autonomous Group Particle swarm optimizationMaksimum güç noktası takibi

Değiştirilmiş Sepic Dönüştürücü

Deterministik Parçacık sürüsü optimizasyonu

Otonom Grubu Geliştirin Parçacık sürüsü optimizasyonu

Sayı

Makale detayları

Yazarlar: Muhammed Mahho | Mehmet Yılmaz | Muhammedfatih Corapsiz

Yayıncı: Bingol University

Ana Başlıklar: Fotovoltaik Güç Sistemleri

Dergi: Türk Doğa ve Fen Dergisi Vol.14 Issue 3, pp.57-66

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001359151

pISSN: 2149-6366

Sayı

Yazarlar:

Muhammed Mahho | Mehmet Yılmaz | Muhammedfatih Corapsiz

Yayıncı:

Bingol University

Ana Başlıklar:

Fotovoltaik Güç Sistemleri

Dergi:

Türk Doğa ve Fen Dergisi Vol.14 Issue 3, pp.57-66

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001359151

pISSN:

2149-6366