Research on fault diagnosis of microgrid based on variational sparse Bayesian fuzzy H-network

Designing a robust and accurate model for consumer centric short term load forecasting in microgrid environment

Aoran Xu, Cailian Gu, Baoliang Liu and Xuemin Leng

International Journal of Low-Carbon Technologies, 2022, vol. 17, 1-10

Abstract: With the aggravation of energy crisis and environmental problems, microgrid has become a research and application hotspot in the field of electric energy, which puts forward higher requirements for the security and reliability of microgrid, so the research on fault diagnosis of microgrid has become the focus. In this paper, the domestic and foreign fault diagnosis research is analysed. Based on Bayesian, the theory of variational sparse Bayesian and fuzzy h-network is studied. The variational sparse Bayesian fuzzy h-network is used for fault diagnosis of microgrid, and the fault diagnosis model of variational sparse Bayesian fuzzy h-network is constructed. Based on the analysis of Bayesian signal processing, a diagnosis method combining fuzzy h-network and variational sparse Bayesian is proposed. The variational sparse algorithm is used to improve Bayesian, reduce the complexity and computation of signal processing and solve the uncertainty problem of microgrid fault diagnosis. The dual fault signal filter based on Bayesian is designed, and the fault diagnosis method of microgrid based on variational sparse Bayesian fuzzy h-network is further studied and designed. The effectiveness of the proposed signal extraction method and fault diagnosis method is verified by simulation in Matlab/Simulink platform. In this paper, a new fault diagnosis method of microgrid based on variational sparse Bayesian fuzzy h-network is proposed, which improves the speed and accuracy of microgrid fault diagnosis.

Keywords: Matlab/Simulink; variational sparse algorithm; fault diagnosis model; Bayesian fuzzy H-network; microgrid (search for similar items in EconPapers)
Date: 2022
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