A switchgear fault prediction method based on a combination of XGBoost and dynamic thresholding

To meet the ever-increasing power supply reliability requirements of the power system, this paper proposes an XGBoost fault warning algorithm based on normal operation data for switchgear. The method effectively analyses a large amount of data by establishing a temperature prediction model using the XGBoost algorithm, which is then combined with a dynamic threshold algorithm for fault determination. In real engineering cases, through the validation of historical data, we verify the significant advantages of the method in terms of prediction accuracy and real-time performance. This method, which integrates multiple technical tools, is expected to provide accurate fault warnings for power operation and maintenance, thus ensuring the safe operation of the power system.