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A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers

Author

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  • Qunli Wu

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping, Beijing 102206, China)

  • Hongjie Zhang

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

Abstract

The fault diagnosis of power transformers is of great significance to improve the reliability of power systems. This paper proposes a novel fault diagnosis method called the expertise-guided machine learning (EGML) model where a genetic algorithm (GA) and a mind evolutionary algorithm (MEA) are used as optimization algorithms. Thereby, two types of EGML models are generated, that is, the GA-EGML model and the MEA-EGML model. In the EGML model, knowledge function replaces the cost function of traditional artificial intelligence algorithms, which can provide additional information for each individual and bring some corrections to the prediction results. To investigate the application potentials of the proposed models in power transformer fault diagnosis, real dissolved gases data are utilized to evaluate the diagnosis performance of the proposed models. Results indicate that the performance of the EGML model outperforms the traditional back propagation neural network (BPNN) model and all other models participating in the comparison. Both the GA-EGML model and MEA-EGML model can be used to diagnose the faults of a power transformer, and the latter is better. In addition, to further investigate the robustness of the proposed models for different data, four scenarios are simulated. Empirical results show that the accuracies of all models decrease in the other three scenarios compared to the baseline scenario, especially in scenario 2. However, the proposed models decline less than the traditional models in scenario 2 and scenario 4, and obtain satisfactory accuracy in all scenarios.

Suggested Citation

  • Qunli Wu & Hongjie Zhang, 2019. "A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1562-:d:213974
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    Cited by:

    1. Guillermo Santamaria-Bonfil & Gustavo Arroyo-Figueroa & Miguel A. Zuniga-Garcia & Carlos Gustavo Azcarraga Ramos & Ali Bassam, 2023. "Power Transformer Fault Detection: A Comparison of Standard Machine Learning and autoML Approaches," Energies, MDPI, vol. 17(1), pages 1-22, December.
    2. Yanzheng Liu & Chenhao Sun & Xin Yang & Zhiwei Jia & Jianhong Su & Zhijie Guo, 2024. "A Transformer Heavy Overload Spatiotemporal Distribution Prediction Ensemble under Imbalanced and Nonlinear Data Scenarios," Sustainability, MDPI, vol. 16(8), pages 1-20, April.
    3. Pedro J. Zarco-Periñán & José L. Martínez-Ramos & Fco. Javier Zarco-Soto, 2021. "On the Remuneration to Electrical Utilities and Budgetary Allocation for Substation Maintenance Management," Sustainability, MDPI, vol. 13(18), pages 1-15, September.

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