Feature extraction based on time-series topological analysis for the partial discharge pattern recognition of high-voltage power cables

In the partial discharge (PD) pattern recognition of power cables, the existing time-frequency features often exert an impact on recognition accuracy because of insufficient discrimination. A novel PD feature extraction and identification method on the basis of time-series topological data analysis (TDA) was proposed in this paper. Firstly, original PD sequence was reconstructed as point cloud in phase space based on optimized symbolic entropy. Then, a PD topological space is constituted with point cloud to extract its persistent homology features. On this basis, persistence diagrams and barcodes were calculated and visually expressed as Betty curves. Finally, Betty curves were input into an optimized 1D convolution neural network (1D-CNN) model to recognize four typical PD patterns and carry out comparison experiments. The visualization produced by t-distributed stochastic neighbor embedding (t-SNE) shows that TDA features possess significant discrimination, experiencing an in-crease of 11.25% in the overall recognition accuracy and reaching 98.00% compared with original PD sequence and time-frequency features. Meanwhile, the computation cost of the proposed algorithm is optimized within the permissible range for real-time applications.