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Non-intrusive temperature rise fault-identification of distribution cabinet based on tensor block-matching

2023-07-26

【论文推荐】中国电科院谈元鹏等：基于张量块匹配的配电柜非侵入性温升故障识别

摘要

在本研究中，提出了一种基于张量块匹配的配电柜非侵入式温升故障识别方法。通过两阶段数据修复重建温度场信息，实现非侵入式配电柜温升故障识别。在粗修复阶段，该方法基于配电柜的外部温度数据，使用张量块匹配技术在温度场张量字典中搜索合适的张量块，从外到内填充目标空间区域，实现了配电柜内部三维温度场的重建。在精修复阶段，利用张量超分辨率技术对粗修复得到的温度场进行填充，实现配电柜内部温度场信息的平滑处理。非侵入式温升故障识别是通过设置聚类规则和温度阈值来比较热源的位置和配电柜组件的位置来实现的。仿真结果表明，与传统技术相比，本文温度场重构误差降低了82.42%，温升故障识别准确率大于86%，验证了温度场重构和温升故障诊断的可行性和有效性。

Non-intrusive temperature rise fault-identification of distribution cabinet based on tensor block-matching

基于张量块匹配的配电柜非侵入性温升故障识别

Jie Tong¹, Yuanpeng Tan¹, Zhonghao Zhang¹, Qizhe Zhang¹,Wenhao Mo¹, Yingqiang Zhang¹,Zihao Qi¹

1.Artificial Intelligence Application Department (China Electric Power Research Institute), Beijing 100192, P.R.ChinaNon-Intrusive Temperature Rise Fault Identification of distribution cabinet based on tensor block-matching

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.

Keywords

Power distribution cabinet; Temperature-field reconstruction; Non-intrusive fault-identification; Compressed sensing; Low-rank tensor

Fig. 1 Diagram of tensor block-matching

Fig. 2 Flowchart of 3D temperature-field reconstruction based on tensor block-matching

Fig. 3 Diagram of tensor super-resolution

Fig. 4 Diagram of temperature rise fault-identification of distribution cabinet based on temperature-field clustering

Fig. 5 Physical simulation platform of distribution cabinet

Fig. 6 Diagram of 2D temperature-profile reconstruction

Fig. 7 Diagram of 3D temperature-field reconstruction

Fig. 8 Diagram of non-intrusive temperature rise faultidentification of distribution cabinet

本文引文信息

Jie Tong, Yuanpeng Tan, Zhonghao Zhang, Qizhe Zhang, Wenhao Mo, Yingqiang Zhang, Zihao Qi (2023) Non-intrusive temperature rise fault-identification of distribution cabinet based on tensor block-matching, Global Energy Interconnection, 6(3): 308-323

仝杰，谈元鹏，张中浩，张启哲，莫文昊，张英强，齐子豪 (2023) 基于张量块匹配的配电柜非侵入性温升故障识别. 全球能源互联网（英文）, 6(3): 324-333

Biographies

Jie Tong

Jie Tong was born in Shanxi, China in 1983.He received his Ph.D.degree in computer system structure from Beijing University of Aeronautics and Astronautics in 2011.He is now working as a professor-level engineer in the Artificial Intelligence Application Department, China Electric Power Research Institute (CEPRI).His research interests include equipment online monitoring, condition assessment, and fault diagnosis.

Yuanpeng Tan

Yuanpeng Tan was born in Tangshan, China in 1987.He received his Ph.D.degree in power information technology from North China Electric Power University in 2017.He is now working as a senior engineer in the Artificial Intelligence Application Department, China Electric Power Research Institute (CEPRI).His research interests include smart sensing, power equipment inspection,knowledge graphs, graph computing, and other technologies.

Zhonghao Zhang

Zhonghao Zhang was born in Shanxi,China in 1991.He received his Ph.D.degree in electrical engineering from Tsinghua University in 2019.Since 2019, he has been an engineer in the Artificial Intelligence Application Department, China Electric Power Research Institute (CEPRI).His research areas include power equipment inspection, deep learning, and fault diagnosis.

Qizhe Zhang

Qizhe Zhang was born in Shanxi, China in 1995.He received his Ph.D.degree in electrical engineering from North China Electric University in 2022.Since 2022, he has been an engineer in the Artificial Intelligence Application Department, China Electric Power Research Institute (CEPRI).His research areas include power equipment inspection, fault diagnosis, and other technologies.

Wenhao Mo

Wenhao Mo was born in Shijiazhuang, China in 1996.He received his Master’s degree in control science and engineering from Harbin Institute of Technology in 2020.Since 2020,he has been an engineer in the Artificial Intelligence Application Department, China Electric Power Research Institute (CEPRI).His research areas include power equipment inspection, deep learning, and image processing.

Yingqiang Zhang

Yingqiang Zhang was born in Xingtai, China in 1990.He received his Master’s degree in computer architecture from NCEPU in 2017.Since 2021, he has been an engineer in the Artificial Intelligence Application Department,China Electric Power Research Institute(CEPRI).His research areas include deep learning, NLP, and electric power knowledge graphs.

Zihao Qi

Zihao Qi was born in Hubei, China in 1999.He received his Bachelor’s degree in computer science from Beijing University of Posts and Telecommunications in 2021.He is now pursuing an M.S.degree in the Artificial Intelligence Application Department, China Electric Power Research Institute (CEPRI).His research areas include power equipment inspection, deep learning, and fault diagnosis.

编辑：王彦博

审核：王伟

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Non-intrusive temperature rise fault-identification of distribution cabinet based on tensor block-matching

【论文推荐】中国电科院 谈元鹏等：基于张量块匹配的配电柜非侵入性温升故障识别

郑重声明

【论文推荐】中国电科院谈元鹏等：基于张量块匹配的配电柜非侵入性温升故障识别