Neural Network Principal Component Analysis based power transformer differential protection

This paper describes a new approach for power transformer protection that ensures the security for external faults, magnetizing inrush and over-excitation conditions and provides dependability for internal faults. This approach based on the wave-shape recognition technique. An algorithm based on Neural Network Principal Component Analysis (NNPCA) with back propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and over-excitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The effect of hidden layer neurons on the classification accuracy is analyzed. The proposed algorithm makes use of ratio of voltage-to-frequency and amplitude of differential current for transformer operating condition detection. The algorithm is evaluated using simulation performed with PSCAD/EMTDC and MATLAB.