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Artificial neural network-based adaptive control for a DFIG-based WECS.

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PSO-GSA based fuzzy sliding mode controller for DFIG-based wind turbine.

N Bounar1, S Labdai2, A Boulkroune1

  • 1LAJ, University of Jijel, BP98, Ouled Aissa, Jijel, 18000, Algeria.

ISA Transactions
|November 4, 2018
PubMed
Summary

This study introduces an optimal fuzzy sliding mode control (FSMC) for doubly fed induction generator (DFIG) wind turbines, enhancing power extraction and grid stability. The novel PSO-GSA optimization method minimizes control issues for improved wind energy systems.

Keywords:
DFIGFuzzy systemsGSAHybrid algorithmOptimizationPSOSliding mode control

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Renewable Energy

Background:

  • Doubly fed induction generators (DFIGs) are crucial for wind turbines.
  • Conventional sliding mode control (SMC) suffers from chattering.
  • Grid code compliance requires reactive power regulation.

Purpose of the Study:

  • To develop an optimal fuzzy sliding mode control (FSMC) for DFIG-based wind turbines.
  • To maximize power extraction while maintaining zero stator reactive power.
  • To mitigate chattering and ensure system stability.

Main Methods:

  • Combining SMC with a fuzzy inference system to reduce chattering.
  • Utilizing the Lyapunov approach for stability analysis.
  • Optimizing fuzzy membership functions with a hybrid Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA).

Main Results:

  • The proposed PSO-GSA based FSMC effectively maximizes power extraction.
  • Zero stator reactive power regulation is achieved, meeting grid requirements.
  • The hybrid optimization significantly improves control performance compared to individual algorithms.

Conclusions:

  • The optimal FSMC strategy provides robust and stable control for DFIG wind turbines.
  • The PSO-GSA algorithm offers superior performance in tuning fuzzy parameters.
  • This approach enhances the efficiency and grid integration of wind energy systems.