## 4 Case Analysis
### Cases of False Alarms in Fault Feature Information  
The fault feature information of the protection system of intelligent substation may be misreported or missed, especially the integrated alarm information, the link information of GOOSE and SV, and the device self-checking information. The sampling value information is obtained by the dual-channel method, and the reliability is high, with almost no false positives or omissions. To improve the diagnostic accuracy in the case of falsely reported fault feature information, this paper extends the training set by adding an appropriate proportion of noise information in the feature information of the fault sample-set data to enhance the generalization ability of the model and reduce the influence of interference information on the final diagnosis. This process was completed in the training process of Section 3.2. The verification shows that when the proportion of noise in each feature information reaches 5%, the generalization ability of the model can meet the requirements. To verify the reliability of the method proposed in this paper when the information is falsely reported, randomly select a piece of information from the fault feature information \(A\) or \(C\) or \(I\) of the case in Section 4.1 to invert (the original \"1\" is set to \"0\" or the original \"0\" set to \"1\") to simulate the situation that the fault feature information is falsely reported. In this case, the \"SV alarm of protection device\" is set to \"0\" to simulate information missed, and the \"Sampling anomaly of merging unit\" is set to \"1\" to simulate information misreported. The diagnosis steps are the same as those in Section 4.1, and the output result set \(\mathrm{R}=\{f_{1},f_{2},f_{3},f_{4},\ldots,f_{12}\}=\{0,0,0,1,\ldots,0\}\). The fault is diagnosed as the fault of \"I/O module fault of merging unit (Bus merging unit \(\rightarrow\) Line merging unit)\", and the diagnosis is correct. To verify whether the proposed method has a generally high accuracy in the case of false alarms of fault feature information, 120 sets of fault data are selected from the test samples in Section 4.1. These 120 sets of data can obtain the correct diagnosis results through the fault diagnosis of the protection system of an intelligent substation based on GBDT. In each sample of fault feature information, one, two, or three information \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & \(f_{1}\) & \(f_{2}\) & \(f_{3}\) & \(f_{4}\) & \(f_{5}\) & \(f_{6}\) & \(f_{7}\) & \(f_{8}\) & \(f_{9}\) & \(f_{10}\) & \(f_{11}\) & \(f_{12}\) \\ \hline \(P\) & 0.02 & 0.09 & 0.41 & 0.96 & 0.03 & 0.04 & 0.11 & 0.09 & 0.14 & 0.14 & 0.11 & 0.04 \\ \hline \end{tabular}
\end{table} 
Table 10: The output probability of the binary classifier under f_4 fault. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & \(f_{1}\) & \(f_{2}\) & \(f_{3}\) & \(f_{4}\) & \(f_{5}\) & \(f_{6}\) & \(f_{7}\) & \(f_{8}\) & \(f_{9}\) & \(f_{10}\) & \(f_{11}\) & \(f_{12}\) \\ \hline Number of samples & 97 & 89 & 96 & 95 & 97 & 88 & 96 & 96 & 89 & 94 & 95 & 93 \\ Number of correct diagnoses for & 96 & 86 & 96 & 94 & 95 & 87 & 96 & 96 & 89 & 94 & 95 & 93 \\ GBDT & & & & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 11: Fault sample distribution and diagnosis results when fault feature information is complete. samples are randomly selected to invert, respectively, to simulate the situation that the fault feature information has one, two, or three false reports, and provides a comparison of the diagnostic results of GBDT with RNN and RF for one false report. According to the diagnosis process in Section 4.1, the 120 faults are re-diagnosed, and the results are shown in Tables 12-14. It can be seen from Tables 12-14 that when the fault feature information is falsely reported, the GBDT model has a very strong anti-overfitting ability. When there are one and two false alarms in the fault feature information, the diagnostic accuracy is high, which is 97% and 92%, respectively.