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A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier

Author

Listed:
  • Jiake Fang

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
    These authors contributed equally to this work.)

  • Hanbo Zheng

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
    These authors contributed equally to this work.)

  • Jiefeng Liu

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
    National Demonstration Center for Experimental Electrical Engineering Education, Guangxi University, Nanning 530004, China)

  • Junhui Zhao

    (Department of Electrical and Computer Engineering & Computer Science, University of New Haven, West Haven, CT 06516, USA)

  • Yiyi Zhang

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
    National Demonstration Center for Experimental Electrical Engineering Education, Guangxi University, Nanning 530004, China)

  • Ke Wang

    (China Electric Power Research Institute, Haidian District, Beijing 100192, China)

Abstract

Dissolved gas analysis (DGA) is widely used to detect the incipient fault of power transformers. However, the accuracy is greatly limited by selection of DGA features and performance of fault diagnostic model. This paper proposed a fault diagnostic method integrating feature selection and diagnostic model optimization. Firstly, this paper set up three feature sets with eight basic DGA gases, 28 DGA gas ratios and 36 hybrid DGA features, respectively. Then, to eliminate the interference of weak-relevant and irrelevant features, the genetic-algorithm-SVM-feature-screen (GA-SVM-FS) model was built to screen out the optimal hybrid DGA features subset (OHFS) from three feature sets. Next, using the OHFS as the input, the support vector machine (SVM) multi-classifier optimized by ISGOSVM (SVM classifier optimized by improved social group optimization) was built to diagnose fault types of transformers. Finally, the performance of OHFS and ISGOSVM diagnostic model was tested and compared with traditional DGA features and diagnostic models, respectively. The results show that the OHFS screened out is comprised of 14 features, including 12 gas ratios and two gases. The accuracy of OHFS is 3–30% higher than traditional DGA features, and the accuracy of ISGOSVM can increase by 3% to 14% compared with the SGOSVM (SVM classifier optimized by social group optimization), GASVM (SVM classifier optimized by genetic algorithm optimization), PSOSVM (SVM classifier optimized by particle swarm optimization), and SVM diagnostic models. The proposed approach integrating the OHFS with ISGOSVM achieves the highest accuracy of fault diagnose (92.86%).

Suggested Citation

  • Jiake Fang & Hanbo Zheng & Jiefeng Liu & Junhui Zhao & Yiyi Zhang & Ke Wang, 2018. "A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier," Energies, MDPI, vol. 11(8), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:1922-:d:159619
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    References listed on IDEAS

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    Cited by:

    1. Yiyi Zhang & Yuxuan Wang & Xianhao Fan & Wei Zhang & Ran Zhuo & Jian Hao & Zhen Shi, 2020. "An Integrated Model for Transformer Fault Diagnosis to Improve Sample Classification near Decision Boundary of Support Vector Machine," Energies, MDPI, vol. 13(24), pages 1-15, December.
    2. Ning Wang & Fei Zhao, 2020. "An Assessment of the Condition of Distribution Network Equipment Based on Large Data Fuzzy Decision-Making," Energies, MDPI, vol. 13(1), pages 1-13, January.
    3. 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.
    4. Bing Zeng & Jiang Guo & Wenqiang Zhu & Zhihuai Xiao & Fang Yuan & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM," Energies, MDPI, vol. 12(21), pages 1-18, November.
    5. Fenglan Tian & Zhongzhao Jing & Huan Zhao & Enze Zhang & Jiefeng Liu, 2019. "A Synthetic Condition Assessment Model for Power Transformers Using the Fuzzy Evidence Fusion Method," Energies, MDPI, vol. 12(5), pages 1-17, March.
    6. 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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