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Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network

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  • Jiaying Deng

    (College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China)

  • Wenhai Zhang

    (College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China)

  • Xiaomei Yang

    (College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China)

Abstract

To avoid power supply hazards caused by cable failures, this paper presents an approach of incipient cable failure recognition and classification based on variational mode decomposition (VMD) and a convolutional neural network (CNN). By using VMD, the original current signal is decomposed into seven modes with different center frequencies. Then, 42 features are extracted for the seven modes and used to construct a feature vector as input of the CNN to classify incipient cable failure through deep learning. Compared with using the original signals directly as the CNN input, the proposed approach is more efficient and robust. Experiments on different classifiers, namely, the decision tree (DT), K-nearest neighbor (KNN), BP neural network (BP) and support vector machine (SVM), and show that the CNN outperforms the other classifiers in terms of accuracy.

Suggested Citation

  • Jiaying Deng & Wenhai Zhang & Xiaomei Yang, 2019. "Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network," Energies, MDPI, vol. 12(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:2005-:d:234292
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    References listed on IDEAS

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    1. Minh-Tuan Nguyen & Viet-Hung Nguyen & Suk-Jun Yun & Yong-Hwa Kim, 2018. "Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear," Energies, MDPI, vol. 11(5), pages 1-13, May.
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    4. Abdullahi Abubakar Mas’ud & Jorge Alfredo Ardila-Rey & Ricardo Albarracín & Firdaus Muhammad-Sukki & Nurul Aini Bani, 2017. "Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources," Energies, MDPI, vol. 10(7), pages 1-20, July.
    5. Ju Tang & Miao Jin & Fuping Zeng & Siyuan Zhou & Xiaoxing Zhang & Yi Yang & Yan Ma, 2017. "Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance," Energies, MDPI, vol. 10(10), pages 1-14, October.
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    Cited by:

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    2. He, Deqiang & Liu, Chenyu & Jin, Zhenzhen & Ma, Rui & Chen, Yanjun & Shan, Sheng, 2022. "Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning," Energy, Elsevier, vol. 239(PB).
    3. Zou, Yingchao & Yu, Lean & Tso, Geoffrey K.F. & He, Kaijian, 2020. "Risk forecasting in the crude oil market: A multiscale Convolutional Neural Network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    4. Kai Chen & Rabea Jamil Mahfoud & Yonghui Sun & Dongliang Nan & Kaike Wang & Hassan Haes Alhelou & Pierluigi Siano, 2020. "Defect Texts Mining of Secondary Device in Smart Substation with GloVe and Attention-Based Bidirectional LSTM," Energies, MDPI, vol. 13(17), pages 1-17, September.

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