Abstract
Smart grid is increasingly becoming the development trend of grid technology. Smart grid is an important part of smart grid. Power equipment is the core part of smart grid, and the normal or not of power equipment directly affects the safety and stability of the entire power system. Smart smart grid usually has relatively complete fault diagnosis and self-healing functions to improve the stability and reliability of the grid. The application and development of a practical fault monitoring and early warning system for power equipment is to carry out predictive maintenance of power equipment so that the equipment can operate more safely and reliably. Due to the time-varying nonlinearity, stochastic uncertainty and local observability of smart grid, it is difficult for traditional power system modeling and analysis methods to fully reflect the steady- state and transient characteristics of power system in the new form. In this paper, based on machine learning simulation technology, through the research of power equipment fault monitoring system in smart grid, provide early warning information and solutions for equipment management personnel maintenance.
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