Parçacık Sürüsü Optimizasyonu ile Fotovoltaik Sistemlerde Maksimum Güç Noktası Takibi ve Verimlilik Artışı

Abstract

Artan küresel enerji talebi ve çevresel sürdürülebilirlik hedefleri doğrultusunda yenilenebilir enerji kaynaklarına olan ilgi hızla artmaktadır. Bu kaynaklar arasında fotovoltaik (FV) sistemler, doğrudan güneş enerjisinden elektrik üretme kabiliyetleri ve çevre dostu yapıları nedeniyle önemli bir alternatif olarak ortaya çıkmıştır. Ancak, fotovoltaik panellerin çıkış gücü, güneş ışınımı, ortam sıcaklığı ve yük koşulları gibi faktörlere bağlı olarak sürekli dalgalanmakta ve bu da sistem verimliliğinde değişikliklere yol açmaktadır. Bu nedenle, toplam enerji üretimini optimize etmek için sistemi her zaman Maksimum Güç Noktası'nda (MPP) çalıştırmak esastır. Bu çalışmada, fotovoltaik sistemlerde maksimum güç noktasını doğru, hızlı ve kararlı bir şekilde izlemek için Parçacık Sürüsü Optimizasyonu (PSO) tabanlı bir Maksimum Güç Noktası Takibi (MPPT) algoritması önerilmiştir. Doğadan ilham alan meta-sezgisel bir algoritma olan PSO, geleneksel MPPT tekniklerine kıyasla daha yüksek takip doğruluğu, daha hızlı tepki süresi ve iyileştirilmiş kararlılık sunmaktadır. MATLAB/Simulink ortamında yürütülen simülasyon çalışmaları, PSO tabanlı yaklaşımın değişen çevre koşullarında etkili bir performans gösterdiğini ve fotovoltaik sistemlerin enerji verimliliğini önemli ölçüde artırdığını göstermektedir. Sonuçlar, PSO algoritmasının PV sistemlerinin dinamik özelliklerine uyum sağlayabildiğini ve gerçek zamanlı izleme uygulamaları için verimli bir çözüm sunduğunu göstermektedir.

Keywords

• Fotovoltaik Sistemler
• Maksimum Güç Noktası Takibi
• Parçacık Sürüsü Optimizasyonu
• Yenilenebilir Enerji
• Enerji Verimliliği

Maximum Power Point Tracking and Efficiency Increase in Photovoltaic Systems with Particle Swarm Optimization

Abstract

In line with the growing global energy demand and environmental sustainability goals, interest in renewable energy sources has been increasing rapidly. Among these sources, photovoltaic (PV) systems have emerged as a significant alternative due to their ability to generate electricity directly from solar energy and their environmentally friendly nature. However, the output power of photovoltaic panels fluctuates continuously depending on factors such as solar irradiance, ambient temperature, and load conditions, which in turn leads to variations in system efficiency. Therefore, operating the system at the Maximum Power Point (MPP) at all times is essential for optimizing total energy production. In this study, a Particle Swarm Optimization (PSO)-based Maximum Power Point Tracking (MPPT) algorithm is proposed to accurately, rapidly, and stably track the maximum power point in photovoltaic systems. PSO, a nature-inspired metaheuristic algorithm, offers higher tracking accuracy, faster response time, and improved stability compared to conventional MPPT techniques. Simulation studies conducted in the MATLAB/Simulink environment demonstrate that the PSO-based approach performs effectively under varying environmental conditions and significantly enhances the energy efficiency of photovoltaic systems. The results indicate that the PSO algorithm can adapt to the dynamic characteristics of PV systems and serves as an efficient solution for real-time tracking applications.

Keywords

• Photovoltaic Systems
• Maximum Power Point Tracking
• Particle Swarm Optimization
• Renewable Energy;
• Energy Efficiency

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