Evaluation for Switchgear Health States Based on Multi-dimensional Feature with Optimal Rank

被引：0
|
作者：
Yang F. [1 ]
Deng Y. [1 ]
Li D. [1 ]
Zhao Y. [1 ]
机构：
[1] School of Electrical Engineering, Shanghai University of Electrical Power, Shanghai
来源：
Yang, Fan (smartgridcontrol@163.com) | 1600年 / Science Press卷 / 46期
基金：
中国国家自然科学基金;
关键词：
Data visualization; K-means cluster algorithm; Live detection; Multi-dimensional feature; Optimal rank; Switchgear;
D O I：
10.13336/j.1003-6520.hve.20190537
中图分类号：
学科分类号：
摘要：
In order to evaluate the health state of switchgear with the live detection data more objectively and accurately, we propose an evaluation method for health states based on the multi-dimensional features and optimal rank. Firstly, based on the fluctuating characteristics of partial discharge data, the index of fluctuating degree is introduced. Meanwhile, a comprehensive partial discharge multi-dimensional feature database is constructed to fully exploit the characteristics of partial discharge data. Then, the number of optimal rank K in the cluster algorithm is determined by the sum of squared errors, and the K is the optimal rank. This can solve the problem of setting the number of rank subjectively in the cluster algorithm. Also, the cluster algorithm is applied to classify the health state of switchgear. Finally, the t-distributed stochastic neighbor embedding algorithm is used to reduce dimension to visualize the cluster results two-dimensionally to realize the visualization of high-dimensional data. The feasibility of the algorithm is verified by the live detection data, which can effectively improve the classification accuracy of switchgear health states by 10.9%. Thus it can provide a certain theoretical basis for the evaluation of switchgear. © 2020, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：3934 / 3942
页数：8
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