船用开关柜局部放电异常检测  

Insulation State Anomaly Detection of the Marine Switchgear

作　　者：李浩[1] 陈亚杰[1] 杨帆 LI Hao;CHEN Yajie;YANG Fan(Shanghai Marine Equipment Research Institute,Shanghai 200031,China;College of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200090,China)

机构地区：[1]上海船舶设备研究所,上海200031 [2]上海电力大学电气工程学院,上海200090

出　　处：《机电设备》2024年第2期35-41,共7页Mechanical and Electrical Equipment

摘　　要：针对船用开关柜现场带电检测数据,提出了一种基于多维特征量的主成分(PCA)聚类离群算法,对柜体的局部放电程度进行异常识别。首先采用运行时间的年限系数以及局部放电检测数据的离散度、均值距离度和极差度等指标全面量化开关柜局部放电状态程度,构建PCA-多维样本数据集;通过轮廓系数法选择聚类离群算法最佳的簇参数;考虑聚类后各类别之间的密度差异性,引入相对距离量化局部放电的程度,由此实现局放程度异常识别。对现场带电检测实际数据进行实例分析,验证该方法的可行性,为船用开关柜的局部放电状态异常识别提供一定的理论依据。Based on the on-line detection data of the marine switchgear,a principal component analysis clustering outlier algorithm based on multi-dimensional feature quantity is proposed to identify insulation state of the switchgear.Firstly,the life cycle coefficient of the switchgear and the dispersion of the partial discharge detection data is used,the average distance percentage and the maximum volatility to construct the principal component of the multi-component sample database,which is comprehensively quantified the deterioration degree of the insulation state.Then choosing the optimal cluster parameters of outlier algorithm by silhouette coefficient.Considering the density difference between the clusters,the relative distance is proposed to quantify the degree of deterioration of the insulation state to realize the abnormal detection of insulation state of the switchgear.The case analysis of the living detection data is carried out to verify the feasibility of the method,which provides a theoretical basis for the abnormal identification of the insulation state to the marine switchgear.

关 键 词：开关柜 PCA-多维样本 轮廓系数 相对距离 聚类离群 

分 类 号：TM933[电气工程—电力电子与电力传动]