基于EWT-FE分析联合改进SVM算法的GIS局部放电诊断方法*

doi:10.11985/2024.01.040

摘要/Abstract

摘要：

为提高气体绝缘组合电器(Gas insulated switchgear，GIS)局部放电类型诊断的精度，提出了一种基于EWT-FE结合IHPO-SVM算法的GIS局部放电诊断方法。为深度挖掘局部放电信号内部特征，利用经验小波变换(Empirical wavelet transform，EWT)结合模糊熵(Fuzzy entropy，FE)算法对信号进行分解，并提取有效特征量；为提高支持向量机(Support vector machine，SVM)算法自适应能力与分类识别精度，提出利用经过余弦衰减计算方法以及指数下降函数改进的猎人猎物优化(Improved hunter-prey optimizer，IHPO)算法对SVM算法参数进行优化选取；搭建GIS局部放电试验模型，建立基于EWT-FE信号分析结合IHPO-SVM的局部放电识别模型，对所提算法有效性进行验证。试验结果表明，所提算法GIS局部放电类型诊断精度均大于95%，优于传统诊断算法。

关键词: 局部放电, 气体绝缘组合电器, 经验小波变换, 模糊熵, 改进猎人猎物优化算法, 支持向量机算法

Abstract:

To improve the accuracy of partial discharge type diagnosis in of gas insulated switchgear(GIS), a method of partial discharge diagnosis in GIS based on EWT-FE and IHPO-SVM algorithm is proposed. To deeply explore the internal features of the partial discharge signal, the empirical wavelet transform(EWT) combined with the fuzzy entropy(FE) algorithm is used to decompose the signal and extract the effective feature quantity. To improve the adaptive capability and classification recognition accuracy of support vector machine(SVM) algorithm, the cosine decay calculation method and the exponential descent function are used to improve the hunter-prey algorithm, resulting in the improved hunter-prey optimizer(IHPO) algorithm, and the parameters of SVM are optimally selected by this method. Finally, an experimental model of GIS partial discharge is constructed, and a partial discharge identification model based on EWT-FE signal analysis combined with IHPO-SVM is established to verify the effectiveness of the proposed algorithm. The experimental results show that the diagnostic accuracy of the proposed algorithm for partial discharge type of GIS is more than 95%, which is better than the traditional diagnostic algorithm.

Key words: Partial discharge, gas insulated switchgear, empirical wavelet transform, fuzzy entropy, improved hunter-prey optimizer, support vector machine

中图分类号:

TM595

王利猛, 王硕. 基于EWT-FE分析联合改进SVM算法的GIS局部放电诊断方法^*[J]. 电气工程学报, 2024, 19(1): 371-381.

WANG Limeng, WANG Shuo. Partial Discharge in GIS Diagnosis Method Based on EWT-FE Analysis and Improved SVM Algorithm[J]. Journal of Electrical Engineering, 2024, 19(1): 371-381.

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参数	数值和类型
额定电压/kV	160
额定频率/Hz	50
耦合电容量/pF	50
特高频局放检测参数	带宽300~2 000 MHz；灵敏度：1 pC
检波信号接收频率带宽/MHz	0~25
装置自身放电量/pC	≤0.5
变压器变比	1 000：1
绝缘介质	SF₆气体
气体压力/MPa	≤0.35

局放类型	FE1	FE2	FE3
尖端放电	0.044 7	0.024 9	0.015 5
	0.045 7	0.025 3	0.015 2
	0.044 3	0.025 3	0.015 2
气隙放电	0.010 0	0.010 6	0.010 3
	0.009 2	0.010 5	0.010 4
	0.009 4	0.010 5	0.010 4
悬浮放电	0.009 9	0.004 3	0.000 6
	0.009 9	0.004 3	0.000 5
	0.010 0	0.004 4	0.000 4
金属颗粒放电	0.021 1	0.003 1	0.000 9
	0.021 4	0.003 7	0.001 1
	0.020 7	0.003 3	0.001 0

诊断方法	识别率(%)				总识别率(%)
诊断方法	尖端放电	气隙放电	悬浮放电	颗粒放电	总识别率(%)
EWT-FE-IHPO-SVM	100	100	96.67	96.67	98.33
EWT-FE-HPO-SVM	96.67	100	90	86.67	93.33
EWT-FE-PSO-SVM	93.33	93.33	86.67	90.00	91.67
EWT-EM-IHPO-SVM	93.33	96.67	86.67	86.67	90.83
EEMD-FE-IHPO-SVM	93.33	93.33	83.33	83.33	89.17
EMD-FE-IHPO-SVM	100	70	90	86.67	86.67
EWT-AE-IHPO-SVM	96.67	50	90	83.33	80

放电类型	诊断准确率(%)
尖端放电	99.03
气隙放电	97.09
悬浮放电	98.06
金属颗粒放电	96.12