Research on visual digital twin offline fault prediction method for distribution automation terminal equipment

There are many potential fault risks in distribution automation system. If the fault is not diagnosed in time, the power supply quality will be affected. During the actual operation of the distribution system, the directly collected samples may not contain all the fault data, and the collected data is not judged on the abnormality, which makes the fault diagnosis accuracy low. Therefore, in order to improve the accuracy of fault diagnosis, a visual digital twin offline fault prediction method of distribution automation terminal equipment is proposed. Digital twin technology is adopted to collect complete fault data of distribution automation terminal equipment, repair the missing values in the data, and eliminate abnormal data, so as to improve the quality and availability of the data. The concept of half-wave integration value is used to calculate the fault type of current and voltage, the offline fault prediction rules are generated to realize the visual digital twin offline fault prediction. The experimental results show that the proposed method has high fault line correlation probability and good fault prediction effect. It shows that this method can help power system operators to find and handle faults in time and improve the reliability and stability of distribution system.