## 1 Introduction
With the research in machine learning and deep learning algorithms, their application in fault diagnosis is increasingly employed [11]. Reference [12] proposed a research method for the fault location of the secondary device in intelligent substations based on deep learning. According to the device self-checking information, a fault location model for the secondary device based on a recurrent neural network (RNN) was established and the fault location steps were given; however, the data source used had certain limitations, and due to the limitations of the time and accuracy of the algorithm, this method needs to be improved. Reference [13] proposed an intelligent state assessment of the protection systems based on random forest algorithm, but the prediction accuracy and robustness need to be improved, and the requirements for the parameters are high. The above method can basically meet the needs of fault diagnosis, but there are still various problems in its practical application. In summary, the current research on the protection system of intelligent substations still needs to face these problems: There are many types of faults in the protection system, but the correlation between the fault characteristics is weak; the complex components of the protection system equipment and the connection relationship between the different devices cause a large amount of data to be generated when a fault occurs [14]. Conventional methods cannot efficiently and quickly analyze the massive multi-dimensional data; since the fault-feature information may be distorted and lost during the acquisition process, the results obtained by the conventional method fluctuate with the confidence of the fault feature information; In addition, the accuracy of the algorithm also needs to be improved. In order to solve the problems of data source, data processing, fault diagnosis logic, diagnosis method, and fault accuracy rate faced in fault diagnosis, a new fault diagnosis method of an intelligent substation protection system based on a gradient boosting decision tree is proposed. The GBDT algorithm is a supervised ensemble learning method. Through the continuous iteration of the weak prediction model composed of decision trees, the strong prediction model is trained with the goal of minimizing the prediction errors of the previous round. It has extremely high accuracy and a fast convergence speed. Taking the protection system merging unit, intelligent terminal, and protection device as the main body of fault diagnosis, this method used the integrated alarm information, device self-checking information, link information of GOOSE and SV, and the sampling value information as the judgment basis to form the fault feature information set. According to the historical fault feature data and maintenance records, the faults of the protection system are divided into simple faults and complex faults. At the same time, the gradient boosting decision tree (GBDT) intelligent algorithm is used as a diagnostic tool, and the fault diagnosis process of the protection system is proposed to realize the diagnosis of complex faults of the protection system. The effectiveness of the method proposed in this paper is verified by example analysis.