Rüzgar Enerjisi Sistemlerinde PID Ayarlaması İçin GA ve PSO'nun Karşılaştırmalı Analizi

Öz

Bu çalışma, sezgisel algoritmalar kullanılarak 100 kW'lık bir rüzgar enerjisi dönüştürme sisteminde (WECS) çıkış gerilimini düzenlemek için PID kontrolör parametrelerinin optimizasyonuna ve karşılaştırmalı analizine odaklanmaktadır. İncelenen sistem, bir rüzgar türbini, bir Kalıcı Mıknatıslı Senkron Jeneratör (PMSG), üç fazlı diyot doğrultucu, bir DC-DC yükseltici dönüştürücü ve bir ohmik yükten oluşmaktadır. Değişen rüzgar hızlarında yük gerilimi kararlılığını korumak için, yükseltici dönüştürücünün kontrolünde kullanılan PID katsayıları (Kp, Ki, Kd), Genetik Algoritma (GA) ve Parçacık Sürü Optimizasyonu (PSO) yöntemleri kullanılarak belirlenmiştir. Her iki algoritma için de hata minimizasyonuna odaklanan standart bir amaç fonksiyonu tanımlanmıştır. MATLAB/Simulink'te gerçekleştirilen simülasyon sonuçları, her iki yöntemin de sistem performansını önemli ölçüde iyileştirdiğini göstermiştir. Karşılaştırmalı analizler, PSO algoritmasının referans gerilim değerine GA'dan daha hızlı ulaştığını, GA'nın ise referans değer etrafındaki salınımları en aza indirmede daha etkili olduğunu ortaya koymuştur.

Anahtar Kelimeler

• Rüzgar Enerji Sistemleri
• PID Ayarlama
• Genetik Algoritma
• Parçacık Sürü Optimizasyonu

Comparative Analysis of GA and PSO for PID Tuning in Wind Energy Systems

Öz

This study focuses on the optimization and comparative analysis of PID controller parameters for regulating the output voltage in a 100 kW wind energy conversion system (WECS) using heuristic algorithms. The system under investigation consists of a wind turbine, a Permanent Magnet Synchronous Generator (PMSG), a three-phase diode rectifier, a DC-DC boost converter, and an ohmic load. To maintain load voltage stability under varying wind speeds, the PID coefficients (Kp, Ki, Kd) used in the control of the boost converter is determined using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. A standard objective function for minimizing error is defined for both algorithms. Simulation results performed in MATLAB/Simulink showed that both methods significantly improved system performance. Comparative analyses revealed that the PSO algorithm reached the reference voltage value faster than the GA, while the GA was more effective at minimizing oscillations around the reference value.

Anahtar Kelimeler

• Wind Energy Systems
• PID Tuning
• Genetic Algorithm
• Particle Swarm Optimization

Kaynakça

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