## 3 Fault Diagnosis of the Protection System Based on Gradient Boosting Decision Tree (GBDT)
### Training of Fault Diagnosis Model Based on Gradient Lifting Tree  
The GBDT model is trained by using the fault information of the protection system of a typical 110 KV intelligent substation in southern China. Figure 4 shows the information topology between the devices of the protection system of intelligent substation. The protection system includes a line merging unit, a line protection device, a Bus protection device, and an intelligent terminal. The GOOSE/SV message-receiving form between devices is shown in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Message Number** & **Transmit Port** & **Receive Port** & **Form of Message Transmission** \\ \hline
1 & Line merging unit & Line protection device & Point-to-point SV Communication \\
2 & Line merging unit & Measurement and control device & Networking SV Communication \\
3 & Line merging unit & Bus protection device & Point-to-point SV Communication \\
4 & Intelligent terminal & Line protection device & Point-to-point GOOSE \\
5 & Intelligent terminal & Line merging unit & Networking GOOSE \\  & & & Communication \\
6 & Bus protection device & Intelligent terminal & Point-to-point GOOSE \\
7 & Bus protection device & Line protection device & Networking GOOSE \\
8 & Intelligent terminal & Measurement and control device & Communication \\
9 & Line protection device & Intelligent terminal & Point-to-point GOOSE \\  & & & Communication \\
10 & Measurement and control device & Line merging unit & Communication \\
11 & Measurement and control device & Intelligent terminal & Networking GOOSE \\  & & & Communication \\
12 & Intelligent terminal & Bus protection device & Point-to-point GOOSE \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Interval information flow in intelligent substations. Figure 4: Information topology structure of intelligent substation. Select 4200 actual fault samples of this intelligent substation, and the distribution of fault types in the samples is shown in Table 5.  
The GBDT fault diagnosis model is trained by using the fault sample set of the protection system, with 75% of the samples as the training set and 25% as the test set. Use the training set to train the model according to the steps in Section 3.1. Taking the diagnostic accuracy of the test sample set as the optimization index, the model is optimized by adjusting the learning rate \(\sigma\) and the number of iterations M, because these two parameters have the greatest impact on the accuracy of the model. The training results are shown in Table 6.  
It can be seen from Table 6 that the accuracy of the GBDT model for fault diagnosis of the protection system is quite high, and when the number of iterations is 30 and the learning rate is 0.1, the accuracy is the highest, reaching 99.048%. The specific diagnosis results of the test set samples at this time are shown in Table 7.  
Compared with the existing research methods, such as recurrent neural network (RNN) [12] and random forest algorithm (RF) [13], the fault diagnosis accuracy under the same dataset is shown in Table 8: \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**M** & **0.05** & **0.1** & **0.3** & **0.5** & **0.7** & **1** \\ \hline
10 & 0.97619 & 0.98333 & 0.98095 & 0.98095 & 0.97857 & 0.97381 \\
30 & 0.97857 & 0.99048 & 0.98333 & 0.98095 & 0.97857 & 0.97142 \\
50 & 0.97619 & 0.98809 & 0.98571 & 0.98333 & 0.97857 & 0.97381 \\
70 & 0.97619 & 0.98809 & 0.98809 & 0.98333 & 0.98095 & 0.97619 \\
100 & 0.97619 & 0.98571 & 0.98571 & 0.98571 & 0.98095 & 0.97857 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Diagnostic accuracy of GBDT under different parameters.