Diagnosis method of multi-variable criterion based on EMD and PNN for arc fault diagnosis

被引：0

作者：

Su J. ^{[1
]}

Xu Z. ^{[1
]}

机构：

[1] Fujian Key Laboratory of New Energy Generation and Power Conversion, School of Electrical Engineering and Automation, Fuzhou University, Fuzhou

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2019年 / 39卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Characteristic signal extraction; Dimensionless indicator; Electric arc; EMD; Models; Multi-variable crite-rion; PNN;

D O I：

10.16081/j.issn.1006-6047.2019.04.016

中图分类号：

学科分类号：

摘要：

Single-variable criterion methods of arc fault diagnosis are greatly influenced by uncertain factors and difficult to extract the characteristic quantities, aiming at which, a multi-variable criterion based on EMD(Empirical Mode Decomposition) and PNN(Probabilistic Neural Network) is proposed. Time-frequency decomposition of arc current is carried out by EMD analysis method, and the fault characteristic signal is extracted by signal correlation theory automatically. The set of multi-variable characteristic vectors is formed by analyzing the dimensionless index of fault characteristic signals. On this basis, an arc fault diagnosis model based on PNN is established. The accuracy of the proposed model is verified by analyzing current waveforms of kettles, vacuum cleaners, halogen lamps, drills, fluorescent lamps and computers before and after arcing. Results show that the proposed method solves the problems of difficult feature extraction and cross-repetition in single-variable criterion fault diagnosis, and its accurate rate is over 90%. © 2019, Electric Power Automation Equipment Press. All right reserved.

引用

页码：106 / 113

页数：7

共 16 条

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