电子科技大学学报2024，Vol.53Issue(3)：440-446,7.DOI:10.12178/1001-0548.2022398
基于时序拓扑数据分析的电力电缆局部放电模式识别
Power Cable Partial Discharge Pattern Recognition Based on Topological Data Analysis for Time Series
摘要
Abstract
In the partial discharge(PD)patterns recognition of power cables,phase resolved partial discharge and statistical features often affect recognition accuracy due to insufficient discrimination.Therefore,a method based on time series topology data analysis(TDA)for feature extraction and recognition of PD is proposed.Firstly,a method combining symbolic entropy and Particle Swarm Optimization(PSO)for the selection of reconstruction parameters is proposed.The pre-processed PD signal in time-domain is reconstructed in phase space to generate a three-dimensional PD data point cloud.Secondly,based on the TDA method,persistent homology features are extracted to generate persistence diagram and persistence barcodes,which are calculated and visually expressed as Betty curve.Finally,the Betty curve is employed as the input of 1D-CNN model for recognition.The experimental results show that the proposed method is more accurate in the selection of the time-delay parameter for phase space reconstruction,and the TDA features achieve good discriminability.Compared with other models that use phase spectrograms and statistical features as inputs,the overall recognition accuracy can be improved by up to 15.34%,reaching 98.55%.关键词
局部放电/模式识别/相空间重构/拓扑数据分析/卷积神经网络Key words
partial discharge/pattern recognition/phase space reconstruction/topological data analysis/convolutional neural network分类
信息技术与安全科学引用本文复制引用
李自强,李睿,孙抗..基于时序拓扑数据分析的电力电缆局部放电模式识别[J].电子科技大学学报,2024,53(3):440-446,7.基金项目
河南省科技攻关计划项目(202102210092) （202102210092）
河南省高校青年骨干教师项目(2021GGJS056) （2021GGJS056）
