A Health Status Assessment Method of Switchgear in Substation Based on Digital Twin Technology and Random Matrix Model

Abstract

The methods for maintaining and monitoring the condition of the power system have changed significantly with the advancement of modern communication and information technology. A strong foundation for the use of digital twin technology and data science in the diagnosis of switchgear health status is provided by the efficient collection of enormous, high-dimensional equipment operation data. This research proposes a digital twin technology-based and random matrix model-based substation switchgear diagnosis approach. The health status of the switchgear can be estimated by mining the potential value of high-dimensional switchgear operation data and relying on data science techniques like high-dimensional statistical analysis and artificial intelligence. This will give maintenance personnel strong support as they develop response strategies.