Safety Risk Detection Method for Static Operation of Secondary Equipment in Smart Substation Based on Association Rule Mining and Ensemble Learning
Juan Hong, Zhang Hua · 2024
To facilitate the management of equipment health status and improve the reliability of power supply, a safety risk detection method for static operation of secondary equipment in smart substations based on association rule mining and ensemble learning is proposed. The static operation safety risk structure of equipment is constructed, and the constructed data is normalized by using association rule mining and ensemble learning, to identify the static operation safety risk types of secondary equipment in smart substation, obtain the differential risk profile feature set of secondary equipment, and obtain the static operation safety risk detection results of secondary equipment after normalization. The experimental results show that this method can effectively detect the status of secondary equipment in substations, and this method has high throughput performance and can quickly and effectively detect potential security risks.