Fault Prediction Model for Wind Energy Conversion Systems-A Neural Network Approach

This paper develops a Neural Network based implementation of the fault identification on the wind turbine model. The prediction algorithm that would predict the algorithm fault that occurs on the wind energy conversion system (WECS) is completely automated using the neural network (NN) algorithm. The fault occurring in the turbine, drive train and the converter is considered, and the automation is carried out by the use of Back Propagation Network Algorithm (BPNA). Matlab based realization using Simulink and M-file functions is carried out and the results are tabulated. The efficient training algorithm and the testing is carried out on the complete wind energy conversion model. The accuracy and the score parameters of the algorithm is analyzed with the previous implementation and tabulated. The results obtained is inferred for satisfactory results. The faults that would deviate the measured speed, measured torque and conversion system faults are gathered on a single NN model.