AC series arc fault detection based on RLC arc model and convolutional neural network
Jiang, Run and Wang, Yilong and Gao, Xiaoqing and Bao, Guanghai and Hong, Qiteng and Booth, Campbell (2023) AC series arc fault detection based on RLC arc model and convolutional neural network. IEEE Sensors Journal, 23 (13). pp. 14618-14627. ISSN 1530-437X (https://doi.org/10.1109/JSEN.2023.3280009)
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Abstract
AC series arc faults in the power system can lead to electrical fires. However, the generalization performance of the determined detection method would be affected under unknown loads, as current features vary with loads. To address this issue, this article presents a series arc fault detection method based on a high-frequency (HF) RLC arc model and 1-D convolutional neural network (1DCNN). By the current transformer used for receiving differential HF features (D-HFCT), current with complex features is first simplified and divided into different oscillation signal types. Since the types of real D-HFCT data are limited, the RLC arc model is used to generate D-HFCT data with various types of oscillation features by adjusting load types, initial phase angles, and Bernoulli-sequence frequencies. Then, the simulated data are adopted to train the 1DCNN model. Finally, the trained 1DCNN model can detect series arc faults under different types of real loads. Compared with the 1DCNN method driven by the limited types of real-current data, the presented method shows good generalization ability and achieves 99.33% average detection accuracy under nine types of unknown loads, which benefits from the training of simulated D-HFCT data with abundant HF oscillation features.
ORCID iDs
Jiang, Run, Wang, Yilong, Gao, Xiaoqing, Bao, Guanghai, Hong, Qiteng ORCID: https://orcid.org/0000-0001-9122-1981 and Booth, Campbell ORCID: https://orcid.org/0000-0003-3869-4477;-
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Item type: Article ID code: 86091 Dates: DateEvent1 July 2023Published31 May 2023Published Online20 May 2023AcceptedNotes: © 2023 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 > Electrical apparatus and materials > Electric networks Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 11 Jul 2023 07:47 Last modified: 06 Nov 2024 09:45 URI: https://strathprints.strath.ac.uk/id/eprint/86091