Distribution network fault location based on wavelet energy entropy and Alexnet convolutional neural network

Distribution network is generally based on the small current grounding system. When a short-circuit grounding fault occurs, the fault characteristics of the low-current grounding system are not prominent. Considering the access of distributed power supply, the characteristics of current flow in the fault system are quite different from before. The traditional fault section location algorithm based on zero sequence current direction is no longer applicable. Wavelet energy entropy is a signal analysis method based on wavelet transform, which measures the signal entropy by calculating the energy distribution of the signal on different frequency scales, and is suitable for analyzing the fault signal with complex time-frequency characteristics. With the development of artificial intelligence technology, machine learning and larger scale deep learning have become increasingly popular, and the ability to extract data features in massive and complex data sets has been increasing. Therefore, a fault location algorithm based on wavelet energy entropy and Alexnet convolutional neural network is proposed in this paper. In this paper, an improved IEEE33 node fault model with distributed power supply is built with PSCAD/EMTDC, and the fault data of different sections are simulated. The wavelet energy entropy of the fault signal is calculated, and the feature matrix is constructed by combining the wavelet energy entropy of different sensor nodes. Alexnet convolutional neural network is used to train the eigenmatrix, and the parameters of the model are trained, optimized and verified, and the rationality and applicability of the proposed method are demonstrated.