Research on intelligent operation and maintenance of power grid equipment based on deep learning

The health status of main equipment of power grid directly determines the power supply safety and operation reliability of power system. A hybrid prediction model based on LSTM and GRU is proposed to solve the early warning problem of complex faults in power system. The model captures the long-term degradation trend of gas concentration and insulation aging index (72 hours) of oil chromatogram through double LSTM layers and uses GRU layer to respond to sudden abnormalities such as load fluctuation and vibration spectrum efficiently, forming a layered diagnosis mechanism with long-term and short-term characteristics. The input data covers 15-dimensional time series characteristics of equipment body parameters, operating conditions and historical state variables, and is input into the network after standardization and data enhancement pretreatment. Experiments based on five-year monitoring data of 726 transformers show that the hybrid model has AUC of 0.936 in fault prediction task, sMAPE of only 9.7%, and the highest accuracy is improved by 23% compared with single LSTM and GRU models. Meanwhile, the compound fault recognition rate is improved by 41%, and the single prediction response time is 1.8 seconds, which meets the real-time decision-making requirements of electric power. This technology provides active protection capability for superconducting magnetic energy storage power station and other scenarios, realizes closed-loop control from state degradation early warning to maintenance decision, and significantly improves the reliability and timeliness of intelligent operation and maintenance of power grid.