Fault diagnosis in wind power system based on intelligent techniques

الملخص EN

Wind energy is one of the most important sources as well as being environmentally friendly and sustainable.

In this paper, different types of faults of Doubly-Fed Induction Generator (DFIG) have been studied based on Artificial Neural Network (ANN), Particle Swarm Optimization (PSO) and Field Programmable Gate Array.

To simulate the wind generators model MATLAB/Simulink program has been used.

Artificial Neural Network (ANN) is trained for detection the faults and (PSO) technique is used to get the best weights.

After the training process, the network was transformed into a Simulink program and then converted into the Very High Speed Description Language (VHDL) for downloading on the (FPGA) card, which in turn is used to detect and diagnosis the presence of faults where it can be re-programmed with high response and accuracy.