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Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection

Author

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  • Chun Yan
  • Meixuan Li
  • Wei Liu

Abstract

Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H 2 ), methane (CH 4 ), acetylene (C 2 H 2 ), ethane (C 2 H 6 ), and ethylene (C 2 H 4 ). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.

Suggested Citation

  • Chun Yan & Meixuan Li & Wei Liu, 2019. "Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, July.
    • Handle: RePEc:hin:jnlmpe:1019845
    DOI: 10.1155/2019/1019845
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    Cited by:

    1. Kai Ding & Chen Yao & Yifan Li & Qinglong Hao & Yaqiong Lv & Zengrui Huang, 2022. "A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    2. Zhi-Jun Li & Wei-Gen Chen & Jie Shan & Zhi-Yong Yang & Ling-Yan Cao, 2022. "Enhanced Distributed Parallel Firefly Algorithm Based on the Taguchi Method for Transformer Fault Diagnosis," Energies, MDPI, vol. 15(9), pages 1-22, April.
    3. Peihao Yang & Jiarui Chen & Lihao Wu & Sheng Li, 2022. "Fault Identification of Electric Submersible Pumps Based on Unsupervised and Multi-Source Transfer Learning Integration," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    4. Fahad M. Almasoudi, 2023. "Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid," Energies, MDPI, vol. 16(3), pages 1-20, January.

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