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Fault Diagnosis of On-Load Tap-Changer Based on Variational Mode Decomposition and Relevance Vector Machine

Author

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  • Jinxin Liu

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Guan Wang

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Tong Zhao

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Li Zhang

    (Shandong Provincial Key Lab of UHV Transmission Technology and Equipment, Jinan 250061, China)

Abstract

In order to improve the intelligent diagnosis level of an on-load tap-changer’s (OLTC) mechanical condition, a feature extraction method based on variational mode decomposition (VMD) and weight divergence was proposed. The harmony search (HS) algorithm was used to optimize the parameter selection of the relevance vector machine (RVM). Firstly, the OLTC vibration signal was decomposed into a series of finite-bandwidth intrinsic mode function (IMF) by VMD under different working conditions. The weight divergence was extracted to characterize the complexity of the vibration signal. Then, weight divergence was used as training and test samples of the harmony search optimization-relevance vector machine (HS-RVM). The experimental results suggested that the proposed integrated model has high fault diagnosis accuracy. This model can accurately extract the characteristics of the mechanical condition, and provide a reference for the practical OLTC intelligent fault diagnosis.

Suggested Citation

  • Jinxin Liu & Guan Wang & Tong Zhao & Li Zhang, 2017. "Fault Diagnosis of On-Load Tap-Changer Based on Variational Mode Decomposition and Relevance Vector Machine," Energies, MDPI, vol. 10(7), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:946-:d:104069
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    References listed on IDEAS

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    1. Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
    2. Eguchi, Shinto & Copas, John, 2006. "Interpreting Kullback-Leibler divergence with the Neyman-Pearson lemma," Journal of Multivariate Analysis, Elsevier, vol. 97(9), pages 2034-2040, October.
    3. Guoqiang Sun & Tong Chen & Zhinong Wei & Yonghui Sun & Haixiang Zang & Sheng Chen, 2016. "A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks," Energies, MDPI, vol. 9(1), pages 1-16, January.
    4. Nantian Huang & Chong Yuan & Guowei Cai & Enkai Xing, 2016. "Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine," Energies, MDPI, vol. 9(12), pages 1-19, November.
    5. Taiyong Li & Min Zhou & Chaoqi Guo & Min Luo & Jiang Wu & Fan Pan & Quanyi Tao & Ting He, 2016. "Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels," Energies, MDPI, vol. 9(12), pages 1-21, December.
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    Cited by:

    1. Xiaomu Duan & Tong Zhao & Jinxin Liu & Li Zhang & Liang Zou, 2018. "Analysis of Winding Vibration Characteristics of Power Transformers Based on the Finite-Element Method," Energies, MDPI, vol. 11(9), pages 1-19, September.

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