全球能源互联网（英文）2023，Vol.6Issue(3)：324-333,10.DOI:10.1016/j.gloei.2023.06.006
基于张量块匹配的配电柜非侵入性温升故障识别
Non-intrusive temperature rise fault-identification of distribution cabinet based on tensor block-matching
摘要
Abstract
In this study,a novel non-intrusive temperature rise fault-identification method for a distribution cabinet based on tensor block-matching is proposed.Two-stage data repair is used to reconstruct the temperature-field information to support the demand for temperature rise fault-identification of non-intrusive distribution cabinets.In the coarse-repair stage,this method is based on the outside temperature information of the distribution cabinet,using tensor block-matching technology to search for an appropriate tensor block in the temperature-field tensor dictionary,filling the target space area from the outside to the inside,and realizing the reconstruction of the three-dimensional temperature field inside the distribution cabinet.In the fine-repair stage,tensor super-resolution technology is used to fill the temperature field obtained from coarse repair to realize the smoothing of the temperature-field information inside the distribution cabinet.Non-intrusive temperature rise fault-identification is realized by setting clustering rules and temperature thresholds to compare the location of the heat source with the location of the distribution cabinet components.The simulation results show that the temperature-field reconstruction error is reduced by 82.42% compared with the traditional technology,and the temperature rise fault-identification accuracy is greater than 86％,verifying the feasibility and effectiveness of the temperature-field reconstruction and temperature rise fault-identification.关键词
配电柜/温度场重构/非侵入式故障识别/压缩感知/低秩张量Key words
Power distribution cabinet/Temperature-field reconstruction/Non-intrusive fault-identification/Compressed sensing/Low-rank tensor引用本文复制引用
仝杰,谈元鹏,张中浩,张启哲,莫文昊,张英强,齐子豪..基于张量块匹配的配电柜非侵入性温升故障识别[J].全球能源互联网（英文）,2023,6(3):324-333,10.基金项目
This paper is supported by the CEPRI project"Key Technologies for Sparse Acquisition of Power Equipment State Sensing Data"(AI83-21-004)and National Key R&D Program of China(2020YFB0905900). （AI83-21-004）
