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A novel diagnostic algorithm for AC series arcing based on correlation analysis of high-frequency component of wavelet

Yangkun Wang (School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China)
Feng Zhang (School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China)
Shiwen Zhang (School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China)
Guang Yang (School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China)

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering

ISSN: 0332-1649

Article publication date: 3 January 2017

193

Abstract

Purpose

A multi-load available, response reliable and product-friendly method is in urgent need to diagnose the signs of incipient arcing. This paper aims to propose a novel algorithm that originates the application of correlativity analysis of wavelet high-frequency component in state discrimination and further in arcing detection.

Design/methodology/approach

The proposed method calculates the correlation coefficient between the extraction by wavelet transform of arcing series current and that of normal, compares it with a predefined threshold and outputs a trip signal when eight qualified arcing half cycles within a period of 0.5 s are detected.

Findings

Typical appliances are selected in laboratory for arc detection to test the method which carries on independently of impedance type. The algorithm could be optimized to identify arcing for different kinds of loads, including resistive, inductive, capacitive and switching power supply loads, with a same correlation coefficient threshold.

Practical implications

The arithmetic operations of the method are addition and multiplication, which contribute to efficient data computation and transmission for micro-processor to undertake. The reference optimal sampling rate recommended for the algorithm helps to reduce the processed data volume and shows its promising prospect for portable product development.

Originality/value

This proposed correlativity analysis of wavelet transform component algorithm could classify the tested signal into two categories, which benefits the discrimination of normal and fault states in condition monitoring. Laboratory tests prove that it works effectively in arc detection for the commonly used impedance types of loads and needs no offline self-learning or training of samples.

Keywords

Citation

Wang, Y., Zhang, F., Zhang, S. and Yang, G. (2017), "A novel diagnostic algorithm for AC series arcing based on correlation analysis of high-frequency component of wavelet", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 36 No. 1, pp. 271-288. https://doi.org/10.1108/COMPEL-08-2015-0282

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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