首页|基于EWT-FE分析联合改进SVM算法的GIS局部放电诊断方法

基于EWT-FE分析联合改进SVM算法的GIS局部放电诊断方法

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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.
Partial Discharge in GIS Diagnosis Method Based on EWT-FE Analysis and Improved SVM Algorithm
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.

Partial dischargegas insulated switchgearempirical wavelet transformfuzzy entropyimproved hunter-prey optimizersupport vector machine

王利猛、王硕

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现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学) 吉林 132012

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

国家重点研发计划吉林省自然科学基金

2018YFB09044703YDZJ202101ZYTS149

2024

10.11985/2024.01.040

电气工程学报
机械工业信息研究院

电气工程学报

CSTPCD北大核心
影响因子：0.121
ISSN：2095-9524
年,卷(期)：2024.19(1)
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