Partial discharge pattern recognition of HVDC cable based on compressive sensing

被引：0
|
作者：
Yang F. [1 ]
Xu Y. [1 ]
Zheng X. [2 ]
Qian Y. [1 ]
Sheng G. [1 ]
Jiang X. [1 ]
机构：
[1] Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai
[2] Zhoushan Power Supply Company, State Grid Zhejiang Electric Power Company, Zhoushan
来源：
Sheng, Gehao (shenghe@sjtu.edu.cn) | 2017年 / Science Press卷 / 43期
关键词：
Compressive sensing; Homotomy; HVDC XLPE cable; Partial discharge; Sparse representation; ℓ[!sub]1[!/sub]-norm minimization;
D O I：
10.13336/j.1003-6520.hve.20170123014
中图分类号：
学科分类号：
摘要：
Recently, high-voltage direct current (HVDC) cable projects have been developing quickly. However, studies on online detection and pattern recognition of cable and accessories still remain in primary stage. On the basis of cross-linked polyethylene (XLPE) cable, we designed four kinds of partial discharge (PD) defect models, such as air gap inside XLPE, scratch on the surface of XLPE, creepage on the outer semi-conducting layer, and corona inside XLPE on high voltage side. The Sparse Representation Classification technique based on compressed sensing was applied to pattern recognition of PD under DC voltage. The discharge repetition rate plots were drawn as the classification samples. All the training samples composed of an over-complete dictionary and sparse representations of test samples projecting on this dictionary were used to classify based on ℓ1-norm minimization. Three ℓ1-norm minimization algorithms were adopted under different dimensions, including Homotopy algorithm, non-negative least squares (NNLS), and orthogonal matching pursuit (OMP). The results reveal that the recognition rates of the three methods are approximate under lower dimension (10×10 or 15×15). The Homotopy algorithm outperforms the others in recognition rate with the increase of dimension, and achieves 92.31% as the maximum recognition rate under 20×20 dimension. NNLS takes second place while consuming more time. Comprehensive comparison shows that Homotopy algorithm has obvious advantages in recognition accuracy and arithmetic speed, and 20×20 dimension can meet requirements. © 2017, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：446 / 452
页数：6
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