绝缘子沿面放电多光谱脉冲演化特性及诊断方法

doi:10.19595/j.cnki.1000-6753.tces.211357

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摘要

绝缘子或套管处产生的沿面放电是导致开关柜、环网柜绝缘失效的常见诱因,而传统局部放电在线检测和诊断方法难以准确评估放电发展阶段和危害程度。该文采用单光子固态光电传感技术,对绝缘子沿面放电发展过程中的多光谱脉冲特性及其演化规律进行了试验研究,并提出一种基于多光谱脉冲演化特性的沿面放电严重程度诊断方法。首先,搭建多光谱光电同步检测平台,实现了沿面放电过程中多个光谱波段光信号和电流信号的同步采集;然后,根据全波段光脉冲幅值及其导数变化规律对沿面放电发展过程进行了三阶段划分,并分别对沿面放电三阶段多光谱脉冲的相位统计和非相位统计特征进行分析,明确了特征量随放电发展阶段的演化规律;最后,基于深度神经网络算法建立沿面放电多光谱严重程度评估模型,结果显示该模型诊断准确率达到96.75%,验证了多光谱诊断方法的可靠性。

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关键词 ：沿面放电, 光测法, 多光谱诊断, 深度神经网络, 严重程度评估模型

Abstract：

Insulator surface discharge is a common cause of insulation failure of switch cabinets. However, traditional surface discharge detection methods characterized by electromagnetic and acoustic signals can hardly meet the requirements in field application. Based on light radiation, another intrinsic nature of discharge, optical detection method has been applied for discharge diagnosis, performing strong anti-interference and high detection confidence. Further, spectral analysis provides a light wavelength information dimension for refined discharge diagnosis, but it is not practical because of the limitation of sensing equipment. In this paper, a practical discharge spectral detection sensor named SiPM-based multispectral discharge sensor (SMDS) is introduced to study the optical radiation characteristics and the evolution laws of multispectral characteristics during the surface discharge development process of insulators, moreover, a strategy for severity diagnosis of surface discharge based on the characteristics of multispectral pulse evolution are proposed.
First, based on the new generation solid state photoelectric sensors, SMDS with seven specific band channels and one full band channel are adopted to acquire multispectral pulses of surface discharge. Second, the evolution stages of surface discharge are distinguished into three stages, named slight, moderate and severe stage, respectively, according to the derivative of full-band light pulse intensity at different voltages. Third, multispectral characteristics of surface discharge at different stages are analyzed. For the phase-based multispectral characteristics, the light intensity of each band increases significantly with the applied voltage independent of the phase, while the light intensity proportion of each band shows different evolution trends. In the slight stage, the light intensity at 450nm is the largest, accounting for about half of the total light intensity. While the light intensity of 400nm band has the smallest proportion. In the moderate stage, the proportion of light intensity at 450nm decreases slightly, while the proportion of light intensity at 400nm increases slightly. In the severe stage, the proportion of 450nm band decreases obviously with the increase of 400nm band. In terms of the phase distribution, the phase range expands continuously from the slight stage (20°~100° and 175°~280°) to moderate stage (0°~90° and 180°~270°) and to severe stage (0°~90°, 170°~300° and over 340°). At the same time, six characteristics named maximum pulse amplitude, average pulse amplitude, pulse repetition rate and their proportions of each band are defined as the non-phase-based multispectral characteristics. In addition, the first three characteristics perform the significant positive liner correlations with the applied voltage. However, for the maximum pulse amplitude proportion, all the components show a significant correlation with applied voltage, with the 450nm component showing a negative correlation and the 650nm component showing relatively low linearity. For the average pulse amplitude proportion, all the components are significantly correlated with the applied voltage, and the 450nm component is negatively correlated. For the pulse repetition rate proportion, all components show significant correlation with applied voltage, among which 400nm and 450nm show a negative correlation. Finally, discharge risk assessment models with a deep neural networks (DNN) based on the non-phase-based multispectral characteristics are built. The results reveal that the correct rates based on multispectral average pulse amplitude proportion, maximum pulse amplitude proportion and pulse repetition rate proportion are 96.75%, 93.45% and 95.25%, respectively.
The following conclusions can be drawn from the multispectral analysis: ①For the phase-based multispectral characteristics, in the slight stage, the 450nm band has the highest pulse amplitude and the widest phase distribution, while the 500nm wave pulse has the smallest amplitude and narrow phase distribution. With the development of the discharge, the amplitude of the whole band pulse increases obviously in the moderate and severe stage, and the phase distribution shows a significant “left shift”. ②For the non-phase-based multispectral characteristics, with the development of discharge stage, maximum pulse amplitude, average pulse amplitude and pulse repetition rate are significantly increased, and the band maximum pulse amplitude proportion, average pulse amplitude proportion and pulse repetition rate present decrease trends for 450nm and increase trends for other bands. ③The multispectral proportion characteristics can effectively reflect the microscopic characteristics of the discharge development process. The accuracies of the discharge severity evaluation model based on the multispectral proportion characteristics and DNN (over 93%) are higher than that of the traditional phase statistical characteristic models (87.15%).

Key words： Surface discharge optical detection multispectral diagnostic deep neural network severity assessment model

收稿日期: 2021-08-27

PACS:

TM85

基金资助:

国家电网有限公司总部科技项目资助（5500-202218131A-1-1-ZN）

通讯作者: 任明男,1987年生,博士,副教授,研究方向为高压试验技术、状态监测智能传感器、图像融合与故障可视化等。E-mail：renming@xjtu.edu.cn

作者简介: 夏昌杰男,1996年生,博士研究生,研究方向为电力设备状态光学检测及故障可视化。E-mail：xcj19960521@stu.xjtu.edu.cn

引用本文:

任明, 夏昌杰, 余家赫, 李信哲, 董明. 绝缘子沿面放电多光谱脉冲演化特性及诊断方法[J]. 电工技术学报, 2023, 38(3): 806-817. Ren Ming, Xia Changjie, Yu Jiahe, Li Xinzhe, Dong Ming. Multispectral Pulse Evolution Laws of Insulator Surface Discharges and Its Diagnosis Approach. Transactions of China Electrotechnical Society, 2023, 38(3): 806-817.

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https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.211357 https://dgjsxb.ces-transaction.com/CN/Y2023/V38/I3/806

[1] 彭兆裕, 岳丹, 颜冰, 等. 开关柜内沿面放电及电弧放电多光谱特征分析[J]. 绝缘材料, 2021, 54(1): 84-90.
Peng Zhaoyu, Yue Dan, Yan Bing, et al.Multi-spectral characteristics of creeping discharge and arc discharge in switch cabinet[J]. Insulating Materials, 2021, 54(1): 84-90.
[2] 朱明曦, 王黎明. 具有强垂直分量结构沿面放电现象及特征[J]. 电工技术学报, 2020, 35(10): 2266-2274.
Zhu Mingxi, Wang Liming.Surface discharge phenomenon and properties of strong-vertical electric field electrode structures[J]. Transactions of China Electrotechnical Society, 2020, 35(10): 2266-2274.
[3] 李进, 赵仁勇, 杜伯学, 等. 电工环氧绝缘件缺陷无损检测方法研究进展[J]. 电工技术学报, 2021, 36(21): 4598-4607.
Li Jin, Zhao Renyong, Du Boxue, et al.Research progress of nondestructive detection methods for defects of electrical epoxy insulators[J]. Transactions of China Electrotechnical Society, 2021, 36(21): 4598-4607.
[4] 任重, 吴海涛, 肖利龙, 等. 绝缘子表面放电的多物理信号比值演化特征及其诊断方法研究[J]. 绝缘材料, 2020, 53(4): 95-102.
Ren Zhong, Wu Haitao, Xiao Lilong, et al.Multi-physical signals specific value evolution characteristics of surface discharge along insulator and its diagnosis approach[J]. Insulating Materials, 2020, 53(4): 95-102.
[5] 邹阳, 周求宽, 刘明军, 等. 局部放电特高频检测装置抗电磁干扰性能的量化评估方法研究[J]. 电工技术学报, 2020, 35(10): 2275-2282.
Zou Yang, Zhou Qiukuan, Liu Mingjun, et al.Research on quantitative evaluation on anti-electromagnetic interference capability of ultra high frequency partial discharge detection instrument[J]. Transactions of China Electrotechnical Society, 2020, 35(10): 2275-2282.
[6] Kumpulainen L, Hussain G A, Rival M, et al.Aspects of arc-flash protection and prediction[J]. Electric Power Systems Research, 2014, 116: 77-86.
[7] 刘永刚. 光测法检测局部放电的模式识别及放电量估计研究[D]. 重庆: 重庆大学, 2012.
[8] Xia Changjie, Ren Ming, Wang Bing, et al.Infrared thermography based diagnostics on power equipment: state of the art[J]. High Voltage, 2021, 6(3): 387-407.
[9] Ren Ming, Dong Ming, Liu Jialin.Statistical analysis of partial discharges in SF₆ gas via optical detection in various spectral ranges[J]. Energies, 2016, 9(3): 152.
[10] Christophorou L G, Olthoff J K.Electron interactions with SF₆[J]. Journal of Physical and Chemical Reference Data, 2000, 29(3): 267-330.
[11] Tewari S V, Kshirsagar R J, Roy A, et al.Optical emission spectroscopy study on flashover along insulator surface due to particle contamination[J]. Laser and Particle Beams, 2014, 32(4): 681-689.
[12] 任明, 李信哲, 王思云, 等. 基于雪崩面阵的气体绝缘放电光脉冲检测技术及传感性能研究[J]. 电网技术, 2021, 45(9): 3627-3636.
Ren Ming, Li Xinzhe, Wang Siyun, et al.Gas insulated discharge optical pulse detection technology and sensing performance based on avalanche diode array[J]. Power System Technology, 2021, 45(9): 3627-3636.
[13] Ren Ming, Zhou Jierui, Miao Jin.Adopting spectral analysis in partial discharge fault diagnosis of GIS with a micro built-in optical sensor[J]. IEEE Transactions on Power Delivery, 2021, 36(2): 1237-1240.
[14] Ren Ming, Zhang Chongxing, Dong Ming, et al.Fault prediction of gas-insulated system with hypersensitive optical monitoring and spectral information[J]. International Journal of Electrical Power & Energy Systems, 2020, 119: 105945.
[15] 王玉伟, 叶齐政, 郭自清. 基于图像处理技术的交流沿面放电状态诊断方法研究[J]. 中国电机工程学报, 2021, 41(1): 222-230, 412.
Wang Yuwei, Ye Qizheng, Guo Ziqing.AC surface discharge status diagnosis based on image processing techniques[J]. Proceedings of the CSEE, 2021, 41(1): 222-230, 412.
[16] 沈龙, 钱国超, 周仿荣, 等. 一种应用于GIS内部局部放电检测的新型光学传感系统[J]. 光电子技术, 2020, 40(4): 258-264, 276.
Shen Long, Qian Guochao, Zhou Fangrong, et al.A novel optical sensor system for internal partial discharge detection in gas insulation switchgear[J]. Optoelectronic Technology, 2020, 40(4): 258-264, 276.
[17] 姜丰, 朱家玲, 胡开永, 等. Pearson相关系数评价ORC系统蒸发器特性的应用研究[J]. 太阳能学报, 2019, 40(10): 2732-2738.
Jiang Feng, Zhu Jialing, Hu Kaiyong, et al.Applied research to assess envaporator performances in orc system by Pearson correlation coefficient[J]. Acta Energiae Solaris Sinica, 2019, 40(10): 2732-2738.
[18] 姜丰. Pearson相关系数评价混合工质与纳米流体换热特性的应用研究[D]. 天津: 天津大学, 2018.
[19] 朱煜峰, 许永鹏, 陈孝信, 等. 基于卷积神经网络的直流XLPE电缆局部放电模式识别技术[J]. 电工技术学报, 2020, 35(3): 659-668.
Zhu Yufeng, Xu Yongpeng, Chen Xiaoxin, et al.Pattern recognition of partial discharges in DC XLPE cables based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 659-668.
[20] 李超然, 肖飞, 樊亚翔, 等. 基于卷积神经网络的锂离子电池SOH估算[J]. 电工技术学报, 2020, 35(19): 4106-4119.
Li Chaoran, Xiao Fei, Fan Yaxiang, et al.An approach to lithium-ion battery SOH estimation based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119.
[21] Xia Changjie, Ren Ming, Wang Bin, et al.Acquisition and analysis of hyperspectral data for surface contamination level of insulating materials[J]. Measurement, 2021, 173: 108560.
[22] 张慧, 卢文冰, 赵雄文, 等. 基于最小二乘支持向量机和小波神经网络的电力线通信信道噪声建模研究[J]. 电工技术学报, 2018, 33(16): 3879-3888.
Zhang Hui, Lu Wenbing, Zhao Xiongwen, et al.Noise modeling for power line communication channel using the LS-SVM and wavelet neural networks[J]. Transactions of China Electrotechnical Society, 2018, 33(16): 3879-3888.