Online monitoring and fault early warning method for insulation status of power equipment

Abstract

The invention discloses an online monitoring and fault early warning method for insulation status of power equipment, relating to the technical field of power system monitoring. The process comprises the following steps: S1, real-time acquisition of multi-source data from power equipment based on sensors, followed by preprocessing and integration of the dataset; S2, extraction of multi-source data features from the dataset, construction of a multidimensional feature matrix, and data fusion; S3, acquisition of parameter features of power equipment under normal insulation based on historical data, comparison of the extracted multi-source data features with the parameter features under normal insulation from historical data, and construction of an association rule library among features. The invention proposes improving the accuracy of status determination through multi-source data fusion and multidimensional feature extraction combined with dual deviation comparison; verifying the association of abnormal features based on the association rule library to eliminate false anomalies and reduce judgment errors; predicting the degradation trend for the next 3-5 cycles in advance to achieve proactive early warning, reserve sufficient maintenance time, and maintain long-term monitoring effectiveness.