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Enhanced Distributed Parallel Firefly Algorithm Based on the Taguchi Method for Transformer Fault Diagnosis

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  • Zhi-Jun Li

    (State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China
    Guodian Nanjing Automation Co., Ltd., Nanjing 210032, China)

  • Wei-Gen Chen

    (State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Jie Shan

    (School of Electric Power Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Zhi-Yong Yang

    (Guodian Nanjing Automation Co., Ltd., Nanjing 210032, China)

  • Ling-Yan Cao

    (Guodian Nanjing Automation Co., Ltd., Nanjing 210032, China)

Abstract

To improve the reliability and accuracy of a transformer fault diagnosis model based on a backpropagation (BP) neural network, this study proposed an enhanced distributed parallel firefly algorithm based on the Taguchi method (EDPFA). First, a distributed parallel firefly algorithm (DPFA) was implemented and then the Taguchi method was used to enhance the original communication strategies in the DPFA. Second, to verify the performance of the EDPFA, this study compared the EDPFA with the firefly algorithm (FA) and DPFA under the test suite of Congress on Evolutionary Computation 2013 (CEC2013). Finally, the proposed EDPFA was applied to a transformer fault diagnosis model by training the initial parameters of the BP neural network. The experimental results showed that: (1) The Taguchi method effectively enhanced the performance of EDPFA. Compared with FA and DPFA, the proposed EDPFA had a faster convergence speed and better solution quality. (2) The proposed EDPFA improved the accuracy of transformer fault diagnosis based on the BP neural network (up to 11.11%).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3017-:d:798171
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    References listed on IDEAS

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    1. Minghui Ou & Hua Wei & Yiyi Zhang & Jiancheng Tan, 2019. "A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers," Energies, MDPI, vol. 12(6), pages 1-16, March.
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    3. 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.
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    5. Youcef Benmahamed & Omar Kherif & Madjid Teguar & Ahmed Boubakeur & Sherif S. M. Ghoneim, 2021. "Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier," Energies, MDPI, vol. 14(10), pages 1-17, May.
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