Takagi–Sugeno fuzzy based power system fault section diagnosis models via genetic learning adaptive GSK algorithm
作者：
Highlights：
• Improved fault section diagnosis (FSD) method based on T–S FNN is proposed.
• T–S FNN-based diagnosis model is designed for each power system section.
• Enhanced metaheuristic named GLAGSK is presented to optimize T–S FNNs.
• GLAGSK is boosted by adaptive knowledge ratio and genetic learning strategy.
• Proposed FSD method can diagnose different complex fault scenarios accurately.
摘要
•Improved fault section diagnosis (FSD) method based on T–S FNN is proposed.•T–S FNN-based diagnosis model is designed for each power system section.•Enhanced metaheuristic named GLAGSK is presented to optimize T–S FNNs.•GLAGSK is boosted by adaptive knowledge ratio and genetic learning strategy.•Proposed FSD method can diagnose different complex fault scenarios accurately.
论文关键词：Fault diagnosis,Takagi–Sugeno fuzzy neural network,Gaining-sharing knowledge-based algorithm,Parameter adaption
论文评审过程：Received 28 March 2022, Revised 18 August 2022, Accepted 21 August 2022, Available online 28 August 2022, Version of Record 11 September 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2022.109773
