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Research on the generation of localized overheating samples and fault prediction models of transformers and based on brain-inspired spiking neural networks

Author

Listed:
  • Guoliang Zhang
  • Peng Zhang
  • Fei Zhou
  • Zexu Du
  • Jiangqi Chen
  • Zhisong Zhang
  • Qingyu Kong

Abstract

The prediction of transformer failures holds significant importance for maintaining the stability of power systems. This paper investigates the application of a brain-inspired spiking neural network model for fault prediction of transformers. The research is grounded in dissolved gas analysis, employing fuzzy reasoning spiking neural P systems to process fuzzy diagnostic knowledge. It constructs a set of sample data associated with localized overheating and utilizes linguistic variables, membership functions, and an inference rule base to conduct fault analysis. The results indicate that this approach significantly enhances the fault identification and predictive capabilities of transformers.

Suggested Citation

  • Guoliang Zhang & Peng Zhang & Fei Zhou & Zexu Du & Jiangqi Chen & Zhisong Zhang & Qingyu Kong, 2025. "Research on the generation of localized overheating samples and fault prediction models of transformers and based on brain-inspired spiking neural networks," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 436-442.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:436-442.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctae294
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