Application of Data-Driven Method in Fault Prediction of Intelligent Operation and Maintenance System of Photovoltaic Power Station

The purpose of this study is to explore the application of data-driven methods in fault prediction of intelligent operation and maintenance systems of photovoltaic power stations. By building an accurate fault prediction model, the efficiency and safety of power station operation are improved. The historical operation data of photovoltaic power stations are analyzed with the help of long short-term memory networks to predict and diagnose potential faults. The developed model shows high accuracy and recall in the prediction of various types of faults, especially for monitoring system failures and performance degradation, with F1 scores of 0.91 and 0.95 respectively. The research results show that the application of deep learning methods can effectively enhance the fault detection capability of intelligent operation and maintenance systems of photovoltaic power stations, and provide scientific data support and decision-making basis for photovoltaic power station maintenance.