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

Guoliang Zhang, Peng Zhang, Fei Zhou, Zexu Du, Jiangqi Chen, Zhisong Zhang and Qingyu Kong

International Journal of Low-Carbon Technologies, 2025, vol. 20, 436-442

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.

Keywords: transformer; thermal fault; spiking neural networks; fuzzy rules (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ijlct/ctae294 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:436-442.

Access Statistics for this article

International Journal of Low-Carbon Technologies is currently edited by Saffa B. Riffat

More articles in International Journal of Low-Carbon Technologies from Oxford University Press
Bibliographic data for series maintained by Oxford University Press ().