Substation Intelligent Inspection Method Based on Data - Driven

There are many types of power equipment in substations and the operating environment is complex, so there are significant risks during long-term operation. This paper proposes a data-driven intelligent inspection method for substations. The method extracts substation equipment inspection data from the monitoring system configured in the substation, performs preprocessing such as data cleaning, data integration, data conversion, and noise filtering, identifies equipment defect status through the inspection data, and optimizes inspection plans and arranges maintenance based on mining results. Experimental results show that the defect hit rates of this inspection method reach 98.24% in static datasets and 95.17% in dynamic actual scenarios, and the actual inspection effect is good.