A Novel Signal Localized Convolution Neural Network for Power Transformer Differential Protection

This paper presents a novel signal localized convolution neural network (SLCNN) for the power transformer differential protection. The distinct signal localization is performed with the convolution process sequentially on the frequency and time coefficients which are obtained from the wavelet decomposition of the differential current signal. The SLCNN is trained with a modified back-propagation algorithm according to SLCNN's architecture. Three power transformer test systems are considered for evaluation of the proposed SLCNN. The training patterns of each transformer are generated for various operating conditions. The SLCNN for each transformer is trained, validated and tested using its corresponding patterns. Then the performance of SLCNN is evaluated through confusion matrix analysis and is also compared with long short-term memory (LSTM) deep neural network, support vector machine (SVM), conventional back-propagation neural network (CBPNN) and conventional biased restraint second harmonic (CBSH) blocking method.

Automated summary

This paper introduces a novel signal localized convolution neural network (SLCNN) for power transformer differential protection. Signal localization is achieved by sequentially performing convolution on frequency and time coefficients obtained from wavelet decomposition, and the SLCNN is trained with a modified back-propagation algorithm based on its architecture. Evaluation is carried out on three power transformer test systems, with training patterns generated for various operating conditions for each transformer.