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Updated: Sep 13, 2025

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Enhancing grid connected wind energy conversion systems through fuzzy logic control optimization with PSO and GA

Abdelhalim Borni1, Noureddine Bessous2, Layachi Zaghba1

  • 1Centre de Développement des Energies Renouvelables, Unité de Recherche Appliquée en Energies Renouvelables, URAER, CDER, Ghardaïa, 47133, Algeria.

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|July 29, 2025
PubMed
Summary

This study optimizes fuzzy logic controllers for wind turbines using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The optimized fuzzy logic controller (FLC) with PSO demonstrates superior performance in maximizing power output and system stability.

Keywords:
FLCGAGridMembership functionP&OPSOWind energy conversion system

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Area of Science:

  • Electrical Engineering
  • Renewable Energy Systems
  • Control Systems

Background:

  • Wind energy conversion systems (WECS) face challenges with variable wind speeds and grid fluctuations.
  • Efficient Maximum Power Point Tracking (MPPT) is crucial for maximizing energy yield from wind turbines.
  • Conventional MPPT methods like perturb-and-observe (P&O) have limitations in dynamic conditions.

Purpose of the Study:

  • To design and simulate an optimized fuzzy logic MPPT controller for grid-tied wind turbines.
  • To compare the performance of optimized fuzzy logic controllers (FLC) against conventional methods.
  • To evaluate the effectiveness of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in enhancing FLC performance.

Main Methods:

  • Implemented four MPPT methodologies: P&O, standalone FLC, FLC-GA, and FLC-PSO.
  • Utilized PSO and GA to optimize the input and output membership functions of the FLC.
  • Conducted simulations to analyze system performance under varying wind conditions.

Main Results:

  • The FLC-PSO approach demonstrated faster convergence (0.05s transient time) and higher tracking accuracy compared to FLC-GA and P&O.
  • Optimized FLCs effectively mitigated issues like distortion and abrupt wind speed variations.
  • FLC-PSO offered superior efficiency, stability, and computational simplicity over FLC-GA.

Conclusions:

  • Optimized fuzzy logic controllers, particularly FLC-PSO, significantly enhance WECS performance.
  • PSO is a preferred optimization technique for FLC-MPPT due to its speed and accuracy.
  • The proposed FLC-PSO method represents an advancement in renewable energy integration and grid management.