摘要
气体绝缘组合开关设备(Gas Insulated Switchgear,GIS)是电力系统中的关键设备,通过局部放电信号获取GIS设备内部绝缘缺陷的放电类型对于故障诊断和预警至关重要。通过试验提出了一种GIS设备不同放电缺陷类型的识别方法,首先搭建了GIS内典型缺陷模型并使用特高频传感器获取局部放电信号,随后对GIS局部放电的三维PRPS图谱进行Gabor变换,并对分解图提取纹理和形状特征量。使用层次聚类法对提取的特征量进行聚类分析,聚类结果表明特征量与缺陷的放电特征具有较好的关联程度,验证了根据PRPS图谱经Gabor变换后提取特征量对典型缺陷进行识别的可行性。基于此,对不同典型放电缺陷使用不同种类的机器学习算法对放电类型进行识别。结果表明:多种机器学习算法对3种典型放电类型的识别准确率较高,PRPS图谱经Gabor变换后提取出的特征量能够较好地反映放电特征,具有较高的区分度,该诊断方法能对GIS故障预警提供可靠的参考依据。
Gas insulated switchgear(GIS)is a key equipment in power system. Obtaining the discharge type of insulation defects in GIS equipment through partial discharge signal is very important for fault diagnosis and early warning. A method for identifying different types of discharge defects in GIS equipment through experiments was proposed. Firstly,typical defect models in GIS were built and UHF sensors were applied to obtain partial discharge signals. Then,Gabor transform was performed on the threedimensional partial discharge PRPS patterns of GIS and texture and shape feature characteristics of the decomposition patterns were extracted. The hierarchical clustering method was used to cluster the extracted feature quantity. The clustering results show that the feature quantity has a good correlation with the discharge characteristics of defects,and verify the feasibility of identifying typical defects according to the feature quantity extracted from PRPS patterns by Gabor transform. Different kinds of machine learning algorithms were used to identify the discharge types for different typical discharge defects.The results show that the recognition accuracy of the 3 typical discharge types by various machine learning algorithms is high,and the extracted feature characteristics of PRPS patterns after the Gabor transform well reflect the discharge characteristics with high differentiation. The proposed diagnosis method can provide a reliable reference basis for GIS fault warning.
作者
李杰
汪鹏
孙景文
孙艳迪
孙承海
LI Jie;WANG Peng;SUN Jingwen;SUN Yandi;SUN Chenghai(State Grid Shandong Electric Power Research Institute,Jinan 250003,China)
出处
《山东电力技术》
2022年第2期54-60,66,共8页
Shandong Electric Power
基金
国网山东省电力公司科技项目“设备用固体绝缘件状态综合评价技术研究”(2021A-076-GIS)。
关键词
GIS设备
局部放电
GABOR变换
聚类分析
模式识别
机器学习
GIS equipment
partial discharge
Gabor transform
clustering analysis
pattern recognition
machine learning
