## 5 Conclusions  
This paper proposed a digital twin-based protection system state evaluation and fault prediction method, which created a new way of thinking for the application of digital twin technology in intelligent substations. This paper reviewed the digital twin architecture of protection system, which consisted of five parts: a physical device layer, a data transmission layer, a data-processing layer, a twin model layer and an application management layer. Digital twin workflow for protection systems capable of real-time updates and closed-loop data was proposed. Based on the matter-element method and clustering algorithm, a real-time state evaluation and fault prediction model of the protection system was constructed within the digital twin layer. It was verified through practical cases that the method could monitor and evaluate the operation status of protection equipment in real time, and the results obtained from the evaluation matched the actual condition of the equipment. When 600 sets of actual fault records of protection equipment were compared with the fault results obtained by this method, the correct rate of prediction could reach more than 90%. It was also proved that the method could optimize the algorithm and database according to each prediction result, and continuously improved the correctness of the prediction algorithm. In this paper, nine main characteristic quantities and nine common faults for state evaluation and fault prediction were selected by analyzing the actual operation data of a smart station in recent years. This research method is not limited to the several characteristic quantities and fault sets in the paper, but can be modified according to the actual operation features, the development of smart grid technologies, and the renewal of elec  
Figure 11: Accuracy Change of Fault Prediction Algorithm. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Fault Types** & \⃝1 & \⃝2 & \⃝3 & \⃝4 & \⃝5 & \⃝6 & \⃝7 & \⃝8 & \⃝5 & **Total** \\ \hline Actual number of faults & 15 & 15 & 10 & 10 & 30 & 10 & 15 & 30 & 15 & 150 \\ Number of correct & 13 & 13 & 10 & 9 & 28 & 9 & 14 & 27 & 13 & 136 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Number of correct fault predictions for the first set of data. trical equipment. Subsequent research can also be modularized and regionalized for the protection system, and different matter-elements can be established for different devices of the protection system for state evaluation and fault prediction, thus the algorithm model can be refined and optimized. This method can also be applied to other electrical equipment, such as measurement and control devices, communication devices, and distribution automation devices, etc. In the current practical application of relay protection status evaluation, static indicators are generally used, and the evaluation results focus on the management and regular maintenance of the equipment, while it is difficult to grasp the operating status and trend of the equipment in real time, not to mention the possible failure of the equipment to make predictions. In comparison, the digital twin-based protection system state evaluation and fault prediction method proposed in this paper takes the dynamic characteristics of the protection system's operating state information and alarm signals as evaluation indicators, to characterize the operating state of the physical equipment in real time with the changes in the sequence of characteristic quantities. When the status rating is poor, it can make timely and effective fault advance warnings and determine the most likely type of fault, so as to guide operation and maintenance personnel to carry out targeted and preventive maintenance. This method can assist operation and maintenance personnel in decision-making, greatly reducing the workload of operation and maintenance personnel in fault finding, troubleshooting and positioning, and improving the efficiency of protection system status maintenance.