-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.
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