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Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network

Author

Listed:
  • Yichen Zhou

    (College of Qianhu, Nanchang University, Nanchang 330031, China)

  • Xiaohui Yang

    (College of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Lingyu Tao

    (College of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Li Yang

    (College of Information Engineering, Nanchang University, Nanchang 330031, China)

Abstract

Dissolved gas analysis (DGA) based in insulating oil has become a more mature method in the field of transformer fault diagnosis. However, due to the complexity and diversity of fault types, the traditional modeling method based on oil sample analysis is struggling to meet the industrial demand for diagnostic accuracy. In order to solve this problem, this paper proposes a probabilistic neural network (PNN)-based fault diagnosis model for power transformers and optimizes the smoothing factor of the pattern layer of PNN by the improved gray wolf optimizer (IGWO) to improve the classification accuracy and robustness of PNN. The standard GWO easily falls into the local optimum because the update mechanism is too single. The update strategy proposed in this paper enhances the convergence ability and exploration ability of the algorithm, which greatly alleviates the dilemma that GWO is prone to fall into local optimum when dealing with complex data. In this paper, a reliability analysis of thirteen diagnostic methods is conducted using 555 transformer fault samples collected from Jiangxi Power Supply Company, China. The results show that the diagnostic accuracy of the IGWO-PNN model reaches 99.71%, which is much higher than that of the traditional IEC (International Electrotechnical Commission) three-ratio method. Compared with other neural network models, IGWO-PNN also has higher diagnostic accuracy and stability, and is more applicable to the field of transformer fault diagnosis.

Suggested Citation

  • Yichen Zhou & Xiaohui Yang & Lingyu Tao & Li Yang, 2021. "Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network," Energies, MDPI, vol. 14(11), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3029-:d:560891
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    References listed on IDEAS

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    1. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
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    Cited by:

    1. Yichen Zhou & Xiaohui Yang & Lingyu Tao & Li Yang, 2023. "Correction: Zhou et al. Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network. Energies 2021, 14 , 3029," Energies, MDPI, vol. 16(7), pages 1-3, April.
    2. Mohammad H. Nadimi-Shahraki & Shokooh Taghian & Seyedali Mirjalili & Laith Abualigah, 2022. "Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study," Mathematics, MDPI, vol. 10(11), pages 1-24, June.
    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. Ruijun Guo & Guobin Zhang & Qian Zhang & Lei Zhou & Haicun Yu & Meng Lei & You Lv, 2021. "An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique," Energies, MDPI, vol. 14(16), pages 1-18, August.
    5. Sara Mantach & Abdulla Lutfi & Hamed Moradi Tavasani & Ahmed Ashraf & Ayman El-Hag & Behzad Kordi, 2022. "Deep Learning in High Voltage Engineering: A Literature Review," Energies, MDPI, vol. 15(14), pages 1-32, July.

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