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A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network

Author

Listed:
  • Li Zhang

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Wenfang Zhang

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Jinxin Liu

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Tong Zhao

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Liang Zou

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

  • Xinghua Wang

    (School of Electrical Engineering, Shandong University, Jinan 250061, China)

Abstract

Winding hotspot temperature is the key factor affecting the load capacity and service life of transformers. For the early detection of transformer winding hotspot temperature anomalies, a new prediction model for the hotspot temperature fluctuation range based on fuzzy information granulation (FIG) and the chaotic particle swarm optimized wavelet neural network (CPSO-WNN) is proposed in this paper. The raw data are firstly processed by FIG to extract useful information from each time window. The extracted information is then used to construct a wavelet neural network (WNN) prediction model. Furthermore, the structural parameters of WNN are optimized by chaotic particle swarm optimization (CPSO) before it is used to predict the fluctuation range of the hotspot temperature. By analyzing the experimental data with four different prediction models, we find that the proposed method is more effective and is of guiding significance for the operation and maintenance of transformers.

Suggested Citation

  • Li Zhang & Wenfang Zhang & Jinxin Liu & Tong Zhao & Liang Zou & Xinghua Wang, 2017. "A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network," Energies, MDPI, vol. 10(12), pages 1-13, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1998-:d:121194
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    References listed on IDEAS

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    1. Nima Amjady & Farshid Keynia, 2011. "A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems," Energies, MDPI, vol. 4(3), pages 1-16, March.
    2. Zeyu Chen & Rui Xiong & Kunyu Wang & Bin Jiao, 2015. "Optimal Energy Management Strategy of a Plug-in Hybrid Electric Vehicle Based on a Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(5), pages 1-18, April.
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    Cited by:

    1. Xiaomu Duan & Tong Zhao & Jinxin Liu & Li Zhang & Liang Zou, 2018. "Analysis of Winding Vibration Characteristics of Power Transformers Based on the Finite-Element Method," Energies, MDPI, vol. 11(9), pages 1-19, September.
    2. Haonan Tian & Zhongbao Wei & Sriram Vaisambhayana & Madasamy Thevar & Anshuman Tripathi & Philip Kjær, 2019. "A Coupled, Semi-Numerical Model for Thermal Analysis of Medium Frequency Transformer," Energies, MDPI, vol. 12(2), pages 1-16, January.
    3. Chun-feng Xia & Jiang Wu & Wei Wang, 2022. "Design and Study of Mountaineering Wear Based on Nano Antibacterial Technology and Prediction Model," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 17(1), pages 1-16, January.
    4. Sen Zheng & Chongshi Gu & Chenfei Shao & Yating Hu & Yanxin Xu & Xiaoyu Huang, 2023. "A Novel Prediction Model for Seawall Deformation Based on CPSO-WNN-LSTM," Mathematics, MDPI, vol. 11(17), pages 1-22, August.

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