基于IEMD和GA-WNN的断路器分合闸线圈故障诊断方法

doi:10.11930/j.issn.1004-9649.202201039

中国电力 ›› 2022, Vol. 55 ›› Issue (5): 111-121.DOI: 10.11930/j.issn.1004-9649.202201039

基于IEMD和GA-WNN的断路器分合闸线圈故障诊断方法

李天辉¹, 庞先海¹, 范辉², 甄利², 顾朝敏¹, 董驰¹

1. 国网河北省电力有限公司电力科学研究院，河北石家庄 050021;
2. 国网河北省电力有限公司，河北石家庄 050021

收稿日期:2022-01-13 修回日期:2022-02-25 出版日期:2022-05-28 发布日期:2022-05-18
作者简介:李天辉（1984—），男，通信作者，博士，高级工程师，从事电力设备状态监测与故障诊断研究。E-mail：zhuhaihuawang@huwor.com.cn
基金资助:
国家电网有限公司科技项目（kjcd2020-003）；国网河北省电力有限公司科技项目（kj2019-067）。

Fault Diagnosis Method for Circuit Breaker Opening and Closing Coil Based on IEMD and GA-WNN

LI Tianhui¹, PANG Xianhai¹, FAN Hui², ZHEN Li², GU Chaomin¹, DONG Chi¹

1. State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China;
2. State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China

Received:2022-01-13 Revised:2022-02-25 Online:2022-05-28 Published:2022-05-18
Supported by:
This work is supported by Science and Technology Project of SGCC (No.kjcd2020-003), Science and Technology Project of State Grid Hebei Electric Power Co., Ltd. (No.kj2019-067).

摘要/Abstract

摘要： 真空断路器二次回路或操动机构运行状态能通过电流曲线特征反映。首先，通过对真空断路器分合闸线圈铁心卡涩、电压异常(过高或过低)和击穿3种常见故障进行实验室模拟，创建了故障电流曲线特征库。其次，利用故障电流信号经过经验模态分解后的经验模态分量中的能量密度乘对应平均周期为恒定常数的性质，提出一种改进经验模态分解方法来提取分合闸线圈电流特征值，并将其作为小波神经网络的输入样本集。并在此基础上，提出一种改进遗传算法与小波神经网络结合的断路器故障诊断方法。该方法利用改进遗传算法对小波神经网络参数进行寻优，旨在解决小波神经网络参数敏感问题，进而提高诊断算法收敛速度和故障诊断准确率。仿真结果表明：与传统小波神经网络诊断方法相比，利用遗传算法改进的小波神经网络方法诊断正确率高达91%，提高了10个百分点。

关键词: 断路器, 分合闸线圈, 改进集合模态分解, 改进小波神经网络, 故障诊断

Abstract: The running state of the secondary circuit or operating mechanism of vacuum circuit breakers can be reflected by the characteristics of current curves. Firstly, three kinds of common faults, including core blockage, abnormal voltage (too high or too low) and breakdown, are simulated in laboratory, and a fault current curve characteristic library is established. Secondly, based on the property that the product of energy density in the inherent mode function of the fault current signals after ensemble mode decomposition and its corresponding average period is a constant, an improved empirical mode decomposition method（IEMD) is proposed to extract the current eigenvalues of the opening and closing coils, which are used as the input sample set of the neural network. On this basis, a circuit breaker fault diagnosis method is proposed by combining the improved genetic algorithm(GA) and wavelet neural network(WNN). This method uses the improved genetic algorithm to optimize the parameters of the neural network in order to solve the problem of parameter sensitivity of the wavelet neural network, thus improving the convergence speed of the diagnosis algorithm and the accuracy of fault diagnosis. Simulation results show that compared with the traditional neural network diagnosis method, the proposed fault diagnosis method has a diagnostic accuracy of 91%, increasing by 10 percentage point.

Key words: circuit breaker, opening and closing coil, improved set modal decomposition, improved wavelet neural network, fault diagnosis

李天辉, 庞先海, 范辉, 甄利, 顾朝敏, 董驰. 基于IEMD和GA-WNN的断路器分合闸线圈故障诊断方法[J]. 中国电力, 2022, 55(5): 111-121.

LI Tianhui, PANG Xianhai, FAN Hui, ZHEN Li, GU Chaomin, DONG Chi. Fault Diagnosis Method for Circuit Breaker Opening and Closing Coil Based on IEMD and GA-WNN[J]. Electric Power, 2022, 55(5): 111-121.

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https://www.electricpower.com.cn/CN/Y2022/V55/I5/111

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基于IEMD和GA-WNN的断路器分合闸线圈故障诊断方法

Fault Diagnosis Method for Circuit Breaker Opening and Closing Coil Based on IEMD and GA-WNN

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基于IEMD和GA-WNN的断路器分合闸线圈故障诊断方法

Fault Diagnosis Method for Circuit Breaker Opening and Closing Coil Based on IEMD and GA-WNN

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