## 5 Conclusions  
This paper sorts out the common faults of the protection system and proposes simple fault types and corresponding fault feature information and complex fault types and corresponding main fault feature information. The integrated alarm information, link information of GOOSE and SV, device self-checking information, and sampling value information that can be used as fault feature information of the protection system of an intelligent substation are sorted out to form a set of fault feature information. The model parameter adjustment of GBDT is completed according to the fault data. The fault diagnosis model of the protection system of an intelligent substation based on GBDT is studied and verified.  
The method proposed in this paper has a high diagnostic accuracy and stronger generalization ability and is more suitable for processing the fault feature data of the protection system of the intelligent substation. The calculation example shows that the overall accuracy of the method proposed in this paper can reach 99.0476%. Compared with the existing methods based on recurrent neural networks and random forest algorithms, the method proposed in this paper has a higher fault diagnosis accuracy. In the case of one false alarm in the fault feature information data, the accuracy rate of the proposed method can reach 97%. In the case of two false alarms in the fault feature information data, the accuracy rate of the proposed method can reach 92%. In the case of three false alarms in the fault feature information data, the accuracy rate of the proposed method can reach 84%. In multiple fault diagnosis, the accuracy of the proposed method is 91.8367%. Through the above analysis, it can be concluded that the method proposed in this paper gives full play  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Type** & **Number of Training** & **Number of Test** & **Number of Correct** \\  & **Samples** & **Samples** & **Diagnoses for Test** \\  & & & **Samples** \\ \hline \(f_{13}\) & 75 & 24 & 23 \\ \(f_{14}\) & 79 & 25 & 24 \\ \(f_{15}\) & 74 & 24 & 20 \\ \(f_{16}\) & 80 & 25 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Multiple fault sample distribution and diagnosis results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fault Type** & **Fault Feature Information** \\ \hline Main DSP module failure of merging & Sampling anomaly of merging unit, \\ unitSampling DSP module failure of merging & Synchronization anomaly of merging unit, SV \\ unit & alarm of merging unit/protection device, \\ Protection locking, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 17: Fault feature information of this case.  
to the high accuracy and anti-overfitting ability of the GBDT algorithm when dealing with device faults in the protection system. Compared with the RNN and RF algorithms, this method is more convenient to adjust algorithm parameters in addition to higher accuracy. Compared with the existing methods, this method also performs very well when faced with bad data (false alarms of fault information, multiple faults). In conclusion, the method proposed in this paper can play a better role in practical applications.  
Conceptualization, W.D. and Q.C.; data curation, W.D. and Y.D.; formal analysis, W.D. and Q.C.; funding acquisition, Q.C.; methodology, W.D., Q.C., and N.S.; project administration, Q.C.; software, W.D.; validation, W.D. and Q.C.; visualization, W.D. and N.S.; writing--original draft, W.D.; writing--review and editing, W.D. and Q.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 5187070349).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript or in the decision to publish the results.