摘要
根据电力设备周围存在多种干扰以及绝缘存在多局放的工况,提出基于单一缺陷的局部放电在线监测与识别方法,并指出实现此系统的关键技术之一是脉冲群快速分类技术,其由脉冲波形特征提取和聚类分析算法组成。针对108次/s的超宽带局部放电脉冲波形–时间序列,重点研究脉冲波形特征提取及分类技术。在分析等效时频算法和模糊C均值聚类算法的基础上,对其做了进一步的改进。基于气体绝缘开关设备(gas insulated switchgear,GIS)的局部放电试验结果表明:采用改进后的算法可以使计算机准确的完成对局放脉冲的的自动分类,并通过在聚类中引入阀值滤除了各子类脉冲中相似性较低的脉冲。这为研制多局放源的在线监及模式系统提供了试验和理论依据。
In view of the presence of a variety of interference and multi partial discharge （PD） around the electrical equipment, the paper proposed multi PD online monitoring and identification method for partial discharge based on a single defect. It was pointed out that one of the key technologies of developing multi PD online monitoring and identification system was the rapid classification pulse groups using pulse waveform feature extraction and clustering analysis algorithm. With respect to 108 times/s ultra-wideband partial discharge pulse waveform-time series, the paper focused on the pulse waveform feature extraction and classification techniques. The improvements on equivalent frequency algorithm and fuzzy C-means clustering algorithm was proposed. Gas insulated switchgear （GIS） partial discharge test results show, that using the improved algorithm the computer can realize accurate partial discharge pulse automatic classification, and through introducing clustering threshold the low similarity subclass pulse pulse can be filtered out. This provides the experimental and theoretical basis for the development of multi-Bureau online monitoring and modelling system.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2013年第28期168-175,25,共8页
Proceedings of the CSEE
关键词
局部放电
在线监测
特征提取
分类
多局放源
等效时频
模糊C均值
partial discharge （PD）
on-line monitoring
feature extraction
classification
multi PD source
equivalent time-frequency
fuzzy C-means （FCM）
