基于声发射特性的玻璃绝缘子污闪预测模型

doi:10.12204/j.issn.1000-7229.2021.05.015

电力建设 ›› 2021, Vol. 42 ›› Issue (5): 138-144.doi: 10.12204/j.issn.1000-7229.2021.05.015

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基于声发射特性的玻璃绝缘子污闪预测模型

王远东¹, 史文江¹, 韩兴波², 蒋兴良², 张超¹, 张志劲²

1.国网内蒙古东部电力有限公司检修分公司,内蒙古通辽市 028000
2.输配电装备及系统安全与新技术国家重点实验室(重庆大学),重庆市 400044

收稿日期:2020-07-09 出版日期:2021-05-01 发布日期:2021-05-06
通讯作者: 韩兴波
作者简介:王远东(1969),男,硕士,高级工程师,主要从事输变电运检管理研究工作;|史文江(1973),男,硕士,高级工程师,主要从事输变电运检管理研究工作;|蒋兴良(1961),男,博士,教授,博士生导师,主要研究方向为电气工程领域高电压与绝缘技术;|张超(1985),男,学士,工程师,主要从事输电运检管理工作;|张志劲(1976),男,博士,教授,博士生导师,主要从事复杂环境下电力系统外绝缘技术研究工作。
基金资助:
国家电网有限公司科技项目(SGMDJX00YJJS1900693)

Prediction Model for Pollution Flashover on Glass Insulator According to Acoustical Characteristics

WANG Yuandong¹, SHI Wenjiang¹, HAN Xingbo², JIANG Xingliang², ZHANG Chao¹, ZHANG Zhijin²

1. State Grid East Inner Mongolia Electric Power Maintenance Company, Tongliao 028000, Inner Mongolia, China
2. State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University), Chongqing 400044, China

Received:2020-07-09 Online:2021-05-01 Published:2021-05-06
Contact: HAN Xingbo
Supported by:
State Grid Corporation of China Research Program(SGMDJX00YJJS1900693)

摘要/Abstract

摘要：

绝缘子污秽闪络是电力系统不可忽视的灾害之一,绝缘子污秽局部放电声信号可以有效反映绝缘子接近污闪的“危险情况”。首先,在人工污秽实验室内进行大量试验,模拟不同可溶污秽附着密度(soluble contamination density,SCD)、不同灰密对玻璃绝缘子声发射信号的影响。之后,提取了污秽放电声发射信号2个典型特征量并分析了其变化规律。然后,建立了基于广义回归神经网络(general regression neural network,GRNN)的绝缘子外绝缘危险度预测模型,提取7个有效声发射特征量作为GRNN模型的输入,以绝缘子污秽闪络的危险度系数作为输出,得到不同可溶物、不同SCD下的预测结果。结果表明:基于声发射特性的GRNN预测模型准确性较高,声发射特征量的变化受到SCD的影响较大,SCD越低,特征量随机性变化越大,GRNN模型的预测准确性随之降低。所提模型为不同污秽度地区采用声发射测量法监测绝缘子外绝缘状况提供了可信度参考。

关键词: 污秽放电, 绝缘子, 声发射, 危险度预测, 广义回归神经网络(GRNN)

Abstract:

Insulator pollution flashover is a main disaster of electrical power system. A large number of artificial pollution tests are investigated under different contamination levels (different soluble contaminants densities or dust densities). According to experiment data, seven acoustic signal characteristics are extracted and analyzed. According to the conclusion, the general regression neural network (GRNN) model of risk degree prediction is established, in which the seven acoustic signal characteristics are as the inputs with the risk degrees used as outputs. It is found that the prediction accuracy is affected by soluble contaminants density mostly. The results show that the greater the soluble contaminants density, the smaller the acoustic signal characteristics’randomness, and the better prediction accuracy can be obtained. The conclusion of this paper provides reference for acoustic monitoring of insulators in different regions with different pollution levels.

Key words: contaminant discharge, insulator, acoustical signal, risk degree prediction, general regression neural network (GRNN)

中图分类号:

TM85

王远东, 史文江, 韩兴波, 蒋兴良, 张超, 张志劲. 基于声发射特性的玻璃绝缘子污闪预测模型[J]. 电力建设, 2021, 42(5): 138-144.

WANG Yuandong, SHI Wenjiang, HAN Xingbo, JIANG Xingliang, ZHANG Chao, ZHANG Zhijin. Prediction Model for Pollution Flashover on Glass Insulator According to Acoustical Characteristics[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(5): 138-144.

图/表 14

图1 玻璃绝缘子试品外观

Fig.1 Appearance of glass insulator

图2 多功能人工气候室试验接线图

Fig.2 The test circuit of multifunctional artificial climate chamber

表1 绝缘子污秽度

Table 1 Pollution level of insulator strings mg/cm2

图3 不同危险度等级下玻璃绝缘子声发射信号波形

Fig.3 Acoustical signal of glass insulator under different risk levels

表2 绝缘子外绝缘危险度分级

Table 2 Pollution level of insulator strings

表3 不同SCD条件下LXY4-160绝缘子声发射特征量统计

Table 3 Acoustical characteristic values of insulator LXY4-160 under different SCD

图4 绝缘子污秽放电声信号平均振幅与SCD的关系

Fig.4 Relation between average amplitude of acoustical signal and SCD

图5 绝缘子污秽放电声信号最大工频半周期包络线面积与SCD的关系

Fig.5 Relation between the biggest half cycle area of acoustical signal and SCD

图6 绝缘子污秽放电声信号平均振幅与灰密的关系

Fig.6 Relation between average amplitude of acoustical signal and NSDD

图7 绝缘子污秽放电声信号最大工频半周期面积与灰密的关系

Fig.7 Relation between the biggest half cycle area of acoustical signal and NSDD

图8 广义回归网络结构

Fig.8 Structure of GRNN network

图9 玻璃绝缘子外绝缘危险度预测GRNN模型流程

Fig.9 Flow chart of GRNN model

表4 不同污秽度玻璃绝缘子危险度GRNN预测结果

Table 4 The GRNN predictions of insulators’ hazard degrees under different SCD

图10 玻璃绝缘子外绝缘危险度预测结果与实际结果对比(ρNaCl=0.25 mg/cm2)

Fig.10 The actual values and GRNN predictions of insulators’ hazard degrees(ρNaCl=0.25 mg/cm2)

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基于声发射特性的玻璃绝缘子污闪预测模型

Prediction Model for Pollution Flashover on Glass Insulator According to Acoustical Characteristics

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