Fault diagnosis of power cables based on wavelet transform and CNN-informer

Hongwei Li; Qilin Wang; Qiyuan Xu

doi:10.1117/12.3024469

13 May 2024 Fault diagnosis of power cables based on wavelet transform and CNN-informer

Hongwei Li, Qilin Wang, Qiyuan Xu

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131595T (2024) https://doi.org/10.1117/12.3024469
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

Accurate identification of early-stage cable faults is a necessary prerequisite for timely elimination of fault hazards. This article proposes a method for power cable fault diagnosis using wavelet transform and CNN-Informer, which can accurately identify early-stage cable faults from overcurrent signals caused by constant impedance faults, capacitor switching, and excitation inrush currents. The method extracts features from the overcurrent signals using wavelet transform and then constructs a CNN-Informer network fusion model. The model is trained by adjusting the network parameters to establish the mapping relationship between input features and class encoding. Simulation results show that the proposed method can effectively classify overcurrent signals and accurately identify early-stage cable faults, demonstrating high practical value in engineering applications. Furthermore, comparative simulation experiments with similar methods conclude that the proposed method in this article achieves higher accuracy and precision.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongwei Li, Qilin Wang, and Qiyuan Xu "Fault diagnosis of power cables based on wavelet transform and CNN-informer", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131595T (13 May 2024); https://doi.org/10.1117/12.3024469

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KEYWORDS

Wavelet transforms

Wavelets

Detection and tracking algorithms

Convolution

Capacitors

Convolutional neural networks

Data modeling

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