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

Abstract

The increase in world population, the development of industrialization and people's 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.

Keywords

• Maximum power point tracking
• Modified Sepic Converter
• Deterministic Particle swarm optimization
• Enhance Autonomous Group Particle swarm optimization

Doğadan Esinlenen Üç Parçacık Sürüsü Optimizasyon Algoritması Modifiye Edilmiş SEPIC Dönüştürücüde PV Sistemler İçin Uygulanması

Abstract

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 DPSO'ya göre yaklaşık yarı yarıya, PSO'ya göre ise yaklaşık üçte iki oranında azaldığı elde edilmiştir.

Keywords

• Maksimum güç noktası takibi
• Değiştirilmiş Sepic Dönüştürücü
• Deterministik Parçacık sürüsü optimizasyonu
• Otonom Grubu Geliştirin Parçacık sürüsü optimizasyonu

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