上海交通大学学报

上海交通大学学报 >

2023 , Vol. 57 >Issue 9: 1176 - 1185

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.299

电子信息与电气工程

基于多光谱阵列的C4F7N/CO2混合气体局部放电光学特征分析与诊断

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  • 1.上海交通大学 电气工程系, 上海 200240
    2.复旦大学 光源与照明工程系, 上海 200433
李泽(1997-),博士生,从事电力设备局部放电状态监测与智能化研究.

收稿日期: 2022-07-25

  修回日期: 2022-09-25

  录用日期: 2022-11-23

  网络出版日期: 2023-03-10

基金资助

国家自然科学基金(62075045)

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Optical Feature Analysis and Diagnosis of Partial Discharge in C4F7N/CO2 Based on Multispectral Array

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  • 1. Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2. Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200433, China

Received date: 2022-07-25

  Revised date: 2022-09-25

  Accepted date: 2022-11-23

  Online published: 2023-03-10

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摘要

局部放电(PD)的光学检测是反映设备绝缘状态的重要方法.C4F7N/CO2混合气体是目前最具有潜力的SF6替代气体,但是缺乏针对该混合气体光学PD特性和诊断方法的研究.构建了一个可采集7个特征波段的PD多光谱阵列检测平台,制作了4种PD缺陷,分析了5种不同比例的C4F7N/CO2混合气体和纯SF6气体条件下多光谱PD特征在相位分布、能量分布和特征堆叠图的异同,提出了一种基于多光谱特征(MF)和最近邻算法(KNN)的PD诊断新方法.实验结果表明,纯SF6故障识别准确率可达96.2%;C4F7N/CO2混合气体的识别率在88%以上,最高准确率为91.1%.该方法对环保型气体绝缘设备的PD诊断具有指导意义,也为传统的PD检测和诊断提供了新思路.

关键词： C4F7N/CO2; 局部放电; 多光谱阵列; 光学检测; 模式识别

本文引用格式

李泽, 钱勇, 臧奕茗, 周小丽, 盛戈皞, 江秀臣 . 基于多光谱阵列的C4F7N/CO2混合气体局部放电光学特征分析与诊断[J]. 上海交通大学学报, 2023 , 57(9) : 1176 -1185 . DOI: 10.16183/j.cnki.jsjtu.2022.299

Abstract

Optical detection of partial discharge (PD) is an important way to reflect the insulation status of equipment. C4F7N/CO2 gas mixture is one of the most potential substitutes for SF6 at present, but there is a lack of research on its optical PD characteristics and diagnostic methods. In this paper, a PD multispectral array detection platform that can collect 7 characteristic bands is constructed, and 4 kinds of PD defects are produced. The similarities and differences of the PD multispectral characteristics in phase distribution, energy distribution, and feature stacking map under the conditions of 5 different ratios of C4F7N/CO2 gas mixture and pure SF6 gas are analyzed. Finally, a novel method of PD diagnosis based on multispectral features (MF) and k-nearest neighbors (KNN) is proposed. The experimental results show that the fault recognition accuracy in pure SF6 can reach 96.2%. The recognition rate of C4F7N/CO2 gas mixture is above 88%, and the highest accuracy rate is 91.1%. This method has a guiding significance for the PD diagnosis of environmentally friendly gas-insulated equipment, and provides a new route for traditional PD detection and diagnosis.

Key words： C4F7N/CO2; partial discharge (PD); multispectral array; optical detection; pattern recognition

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