摘要
局部放电可以反映气体绝缘组合电器(Gas Insulated Switchgear,GIS)内部的绝缘缺陷,对正确识别GIS的放电类型具有重要意义。在GIS重症监护系统研究平台上人工设置4种GIS的典型缺陷。基于4种缺陷不同电压等级下的局部放电样本数据,提取局部放电灰度图像的分析特性作为识别特征量。同时考虑到现场干扰对局部放电信号的影响,利用GK模糊聚类算法对分形特征量进一步处理,以提取隔离干扰后的分析特征量。最后设计了基于LS-SVC的局部放电模式识别器。试验结果表明所提方法能有效识别GIS放电类型,比人工神经网络方法具有识别率高、稳定性好的优点。
The internal insulation defects in gas-insulated switchgear （GIS） can be reflected by partial discharge, so it is significant to recognize the type of partial discharge （PD） in GIS correctly. Four kinds of typical defection of the GIS are designed on the GIS intensive care research system. The extraction of the characteristics of PD gray image is the recognition features based on the PD sample data of the four kinds defects under different voltage levels. At the same time, considering the effect of interference on the partial discharge signal, GK fuzzy clustering algorithm is used to further process fractal feature and to extract the analysis of characteristics. At last, the PD type recognition device is designed based on the LS-SVC. Experimental results show that using the proposed method the PD type within GIS can be correctly recognized. In addition, the method proposed is stable and possesses higher recognition rate than the artificial neural network method.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2014年第20期38-45,共8页
Power System Protection and Control
基金
江苏省高校自然科学研究基金面上项目(13KJB470006)
江苏方天电力技术有限公司科技项目
关键词
气体绝缘组合电器
局部放电
故障识别
G-K模糊聚类
最小二乘支持向量机
gas insulated switchgear （GIS）
partial discharge （PD）
fault identification
GK-fuzzy clustering
least squares supportvector machine
