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.
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