Machine learning approach to detect arc faults based on regular coupling features

Jiang, Run and Bao, Guanghai and Hong, Qiteng and Booth, Campbell (2022) Machine learning approach to detect arc faults based on regular coupling features. IEEE Transactions on Industrial Informatics, 19 (3). pp. 2761-2771. ISSN 1551-3203 (https://doi.org/10.1109/TII.2022.3153333)

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Abstract

During AC series arc faults (SAFs), arcing current features can change or vanish under different conditions. The phenomena make it challenging to detect SAFs. To address the issues, this paper presents a detection model based on regular coupling features (RCFs). After the model is only trained by the samples in single-load circuits, it can detect SAFs under unknown multi-load circuits. To extract RCFs, asymmetric magnetic flux is coupled by passing the live line and the neutral line through the current transformer. According to the unique signals, two time-domain features and one frequency-domain feature are extracted to represent RCFs, including impulse -factor analysis, covariance-matrix analysis and multiple frequency-band analysis. Then, the impulse factor and its threshold are used to preprocess the signals and decrease analysis complexity for the classifier. Finally, the experimental results show that the proposed method has significantly improved generalization ability and detection accuracy in SAF detection.

ORCID iDs

Jiang, Run, Bao, Guanghai, Hong, Qiteng ORCID logoORCID: https://orcid.org/0000-0001-9122-1981 and Booth, Campbell ORCID logoORCID: https://orcid.org/0000-0003-3869-4477;
  • Item type:Article
    ID code:79813
    Dates:
    Date
    Event
    23 February 2022
    Published
    23 February 2022
    Published Online
    13 February 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:03 Mar 2022 12:40
    Last modified:27 Nov 2024 01:20
    URI:https://strathprints.strath.ac.uk/id/eprint/79813
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