OLTC Fault Diagnosis Method Based on Time Domain Analysis and Kernel Extreme Learning Machine

被引：0
|
作者：
Yan, Yan [1 ,3 ]
Ma, Hongzhong [1 ]
Song, Dongdong [2 ]
Feng, Yang [4 ]
Duan, Dawei [1 ]
机构：
[1] College of Energy and Electrical Engineering, Hohai University, China
[2] College of Mechanical Electrical and Engineering, Hebei Normal University of Science and Technology, China
[3] Electric Power Research Institute, State Grid Ningxia Power Co., Ltd., China
[4] Training Center of State Grid Ningxia Electric Power Co., Ltd., China
来源：
Journal of Computers (Taiwan) | 2022年 / 33卷 / 06期
关键词：
Classification (of information) - Failure analysis - Fault detection - Feature Selection - Frequency domain analysis - Knowledge acquisition - Signal processing - Vibration analysis;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Aiming at the problems of limited feature information and low diagnosis accuracy of traditional on-load tap changers (OLTCs), an OLTC fault diagnosis method based on time-domain analysis and kernel extreme learning machine (KELM) is proposed in this paper. Firstly, the time-frequency analysis method is used to analyze the collected OLTC vibration signal, extract the feature information and form the feature matrix; Then, the PCA algorithm is used to select effective features to build the initial optimal feature matrix; Finally, a kernel extreme learning machine optimized by improved grasshopper optimization algorithm (IGOA), is used to handle the optimal feature matrix for classifying fault patterns. Evaluation of algorithm performance in comparison with other existing methods indicates that the proposed method can improve the diagnostic accuracy by at least 7%. © 2022 Computer Society of the Republic of China. All rights reserved.
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页码：91 / 106
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