Research on Environmental Adaptability of Deep Learning Early Warning System for Ground Fault in UHV Converter Station

This paper designs a UHV converter station ground fault early warning system based on deep learning to improve the accuracy of fault detection, response speed and adaptability to complex environments. The system adopts an algorithm model combining improved CNN and LSTM to fully mine the spatial features and time series information in the operating data to achieve accurate prediction and early warning of ground faults. In the data processing process, the CNN module is used to extract spatial features in the operating data, such as the key mode of the fault current waveform, and the LSTM module models the time series changes to capture the law of abnormal signal changes over time. Through this collaborative mechanism, the system can effectively improve the sensitivity to abnormal signals, reduce the false alarm rate, and maintain high performance under complex and changeable operating conditions. To verify the performance of the system, this paper conducts model simulation experiments under various environmental conditions based on the historical operating data and fault samples of the real converter station, covering different load levels, temperature and humidity ranges and other working conditions. The experimental results show that the accuracy of the system in ground fault detection reaches 98.7%, the false alarm rate is reduced to 1.3%, and it can maintain stable early warning capabilities under various environmental conditions.