Abstract：It is essential to identify development stages of air-gap discharge in XLPE cable accurately under operation. It will not only help to prevent the cable fault, but to safeguard operation of power system. In this paper, the test platform and artificial defect sample manufacture method were introduced firstly. Then feature extraction and dimension reduction steps on a large number of experimental samples were described. Based on experimental observation and cluster analysis, the development process of air-gap discharge was classified into four stages accordingly. Aiming at the problems such as long training cycle, high computational complexity and slow convergence rate, this paper proposes an improved K-nearest neighbor (KNN) r classification algorithm weighted by Gaussian function to identify the air-gap discharge stage of XLPE cable. Three kinds of algorithms, namely kernel support vector machine (SVM) based on binary tree, unimproved KNN and improved KNN, were used to identify the random test samples of air gap discharge. Results show that improved KNN algorithm has high recognition accuracy, fast recognition speed and good robustness in processing noisy signals, which is better than the other two algorithms.
陈曦, 骆高超, 曹杰, 毕茂强, 江天炎. 基于改进K-近邻算法的XLPE电缆气隙放电发展阶段识别[J]. 电工技术学报, 2020, 35(23): 5015-5024.
Chen Xi, Luo Gaochao, Cao Jie, Bi Maoqiang, Jiang Tianyan. Development Stage Identification of XLPE Cable Air-Gap Discharge Based on Improved K-Nearest Neighbor Algorithm. Transactions of China Electrotechnical Society, 2020, 35(23): 5015-5024.
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2020, Vol. 35 
