Optimal identification of doubly fed induction generator parameters in wind power system using particle swarm optimizationand artificial neural network

Abstract EN

Wind energy became one of the techniques that attracted much attention worldwide.

The induction generator is used in the exploitation of this energy and converts it into electrical energy because of the advantages that distinguish it from other types of generators.

In this paper, an optimal identification of induction generator parameters is proposed.

Particle Swarm Optimization technique (PSO) trained using Artificial Neural Network (ANN) is used to identify the main parameters of the induction generator in cases of wind speed change, load change and fault cases.

The simulation results obtained indicate that the particle swarm optimization is suitable for neural networks training for controlling of the voltage, frequency and generated power.

The simulation programming is implemented using MATLAB.