Principal Component Analysis (PCA) Based Neural Network for Motor Protection

Abstract

In this work, a real time digital protection algorithm based on PCA and neural network methods is presented for induction motors. The proposed protection algorithm covers internal winding faults, broken rotor bar faults, and bearing faults. Many laboratory experiments have been performed on a specially designed induction motor to evaluate the performance of the suggested protection algorithm. The hybrid protection algorithm uses the instantaneous phase currents. These currents are first preprocessed by PCA to extract distinctive features called residuals. Then the calculated residuals are applied to a feed-forward backpropagation neural network as input vectors. The outputs of the network are winding fault, bearing fault, and normal operating. The proposed algorithm is implemented by using C++ with a NI-DAQ data acquisition board.