Enhancing pitch angle control in variable speed doubly fed induction generator based wind energy conversion system using advanced adaptive neuro-fuzzy approach with particle swarm optimization

Abstract

Pitch angle control is a common method used to smooth out output power fluctuations in the wind turbines. This paper focuses on Particle Swarm Optimization (PSO) based Adaptive Neuro-Fuzzy Inference System (ANFIS) pitch angle control scheme for a variable speed Doubly Fed Induction Generator (DFIG) based wind energy conversion system designed for operation in high wind speed regions. The proposed enhanced adaptive controller comprises both Sugeno type fuzzy inference system (FIS) and neural network architecture. In the Sugeno type FIS, the membership functions are optimized using a revisited PSO method. The primary objective is to control the pitch angle to ensure that generator power and speed operate within desired references, reducing fluctuations and minimizing mechanical blade stress. A MATLAB/SIMULINK model of wind energy conversion system setup is prepared and simulations are conducted using different control methods. The effectiveness of the proposed approach is confirmed based on the simulation results.