电网技术2016，Vol.40Issue(11)：3597-3603,7.DOI:10.13335/j.1000-3673.pst.2016.11.047
基于多变量多尺度云样本熵和模糊支持向量机的开关柜故障分类
Fault Classification for Smart Switchgear Based on Multivariate Multiscale Cloud Sample Entropy and Fuzzy Support Vector Machine
摘要
Abstract
1Multi-source monitoring data of smart switchgear can be used for abnormal state recognition and fault classification to realize efficient operation and management of distribution equipment. In this paper,sensors were used to monitor features such as voltage, current, temperature, flash signal, and so on.Multi-variable multi-time-scale cloud sample entropy fault features of switchgearwere obtained with lower half-trapezoid cloud model to quantify similarity of composite delay vectors of featuring time series and to soften similar tolerance criterion of multi-variable multi-scale sample entropy. This paper used piecewise half-trapezoid cloud model to quantify relationship uncertainty between fault samples. Regional difference and dispersion of sample space were synthesized to calculate sample membership, and classification method based on fuzzy support vector machine was formed to identify different switchgear fault types. Through case analysis on monitored data, correctness of the proposed scheme was validated.关键词
智能开关柜/多变量多尺度云样本熵/模糊支持向量机/云模型/故障分类Key words
smart switchgear/multi-variable multi-scale cloud sample entropy/fuzzy support vector machine/cloud model/fault classification分类
信息技术与安全科学引用本文复制引用
辛业春,崔金栋,周川,王强钢,周念成..基于多变量多尺度云样本熵和模糊支持向量机的开关柜故障分类[J].电网技术,2016,40(11):3597-3603,7.基金项目
吉林省发改委产业发展计划扶持计划(JF2016400305)；吉林杰出青年扶持项目(20150406)；教育厅产业化项目扶持计划(2015103)；吉林省产业创新专项资金项目(2016C074)。Project Supported by the Industry Development Support Plan of Jilin Province Development and Reform Commission (JF2016400305) （JF2016400305）
the Outstanding Youth Support Project of Jilin Province(20150406) （20150406）
