INFO algorithm based MPPT optimization of a photovoltaic system under partial shading conditions

Abstract

Obtaining maximum efficiency from photovoltaic (PV) systems through maximum power point tracking (MPPT) remains an ongoing challenge. In this study, the weighted mean of vector (INFO) algorithm is employed to address and solve the MPPT problem for a photovoltaic system operating under partial shading. Besides INFO algorithm, electric eel optimization (EEFO), red-tailed hawk algorithm (RTHA), and student psychology-based optimization (SPBO) algorithms were also employed, and this study is the first to employ these optimization algorithms for MPPT purposes. The particle swarm optimization (PSO) algorithm, which is frequently employed in MPPT studies, is employed to compare the performance of new metaheuristic algorithms. These algorithms are tested with challenging shading scenarios where the local maximum points (LMPPs) and global maximum power point (GMPP) varied. The performance of these algorithms is evaluated using the Friedman test, which is a statistical test, and performance metrics. According to the findings of the comparison, the INFO algorithm is the most effective among the five algorithms for MPPT optimization under partial shading conditions, and this conclusion is confirmed statistically. Additionally, experimental tests were conducted to evaluate the performance of the INFO algorithm on real hardware. A programmable PV simulator, boost converter, and STM32 board were used. The experiments demonstrated that the algorithm could quickly and stably track the maximum power point.

Keywords

• Friedman Test
• INFO
• Maximum Power Point
• MPPT
• Partial Shading
• Photovoltaic.

Kısmı gölgelenme koşulları altında fotovoltaik sistemlerin INFO algoritması tabanlı MPPT optimizasyonu

Öz

Fotovoltaik (FV) sistemlerden maksimum verim elde etmek için maksimum güç noktası izleme (MPPT) yöntemi önemli bir araştırma konusu olmaya devam etmektedir. Bu çalışmada, kısmi gölgeleme koşulları altında çalışan bir FV sistem için MPPT problemini çözmek amacıyla vektörlerin ağırlıklı ortalaması (INFO) algoritması kullanılmıştır. INFO’ya ek, elektrikli yılanbalığı optimizasyonu, kızıl kuyruklu şahin algoritması ve öğrenci psikolojisine dayalı optimizasyon algoritması da uygulanmış olup, söz konusu optimizasyon algoritmaları MPPT amacıyla ilk kez bu çalışmada kullanılmıştır. Yeni meta sezgisel algoritmaların performansını karşılaştırmak amacıyla, MPPT çalışmalarında yaygın olarak kullanılan parçacık sürü optimizasyonu algoritması da kullanılmıştır. Algoritmalar, yerel maksimum güç noktalarının ve küresel maksimum güç noktasının değişkenlik gösterdiği zorlu gölgeleme senaryolarında test edilmiştir. Algoritmaların performansları, istatistiksel bir test olan Friedman testi ve performans metrikleri kullanılarak değerlendirilmiştir. Karşılaştırma sonuçlarına göre, kısmi gölgeleme koşulları altında MPPT optimizasyonu için en etkili algoritmanın INFO algoritması olduğu belirlenmiş ve bu sonuç istatistiksel olarak doğrulanmıştır. Ayrıca, INFO algoritmasının gerçek donanımda performansını test etmek için deneysel çalışmalar yapılmıştır. Programlanabilir FV simülatörü, yükseltici tip dönüştürücü ve STM32 kartı kullanılmıştır. Deneyler, algoritmanın hızlı ve kararlı şekilde maksimum güç noktasını izlediğini göstermiştir.

Anahtar Kelimeler

• Friedman Testi
• INFO
• Maksimum Güç Noktası
• MPPT
• Kısmi Gölgeleme
• Fotovoltaik

Kaynakça

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