# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 1 Introduction  
### Research Progress of Power System Fault Processing Technology  
With the increase in the complexity and type of the power system, the number of faults in China's power system is increasing. It poses a huge threat to the production, operation, maintenance, and security of China's power system and the stable development of the national economy. At present, the power system is moving in the direction of intelligence and automation. A large number of sensor devices are used for equipment conditionmonitoring. It is of great significance to ensure the safe and stable operation of the power grid to mine the information related to equipment status from the accumulated massive data and judge whether there is a fault or predict a fault. The related technologies of power system fault diagnosis have been developed rapidly. Various methods are used to diagnose power system faults from different perspectives. The fault diagnosis method can be divided into expert system [1; 2], artificial neural network [3; 4], Bayesian network [5], Petri net technology [6], analytic model [7; 8], information fusion [9; 10], and other methods. The expert system simulates the knowledge and experience of experts to deduce and judge, and finally obtain the explanation closest to the actual situation. With the continuous expansion of the power system scale, the construction and maintenance of knowledge base has become increasingly complex. When information is missing or confused, the accuracy of expert system diagnosis is greatly reduced [1; 2]. Artificial Neural Network (ANN) is a technology that can process information by simulating the human nervous system. The ANN obtains fault diagnosis results through information input, but it is difficult to complete the explanation of the whole fault development process [3]. In recent years, the ANN have been widely used in fault area division and fault location, which greatly improved learning efficiency. However, its interpretation ability in the fault diagnosis of a large power grid needs to be further improved [4]. The Bayesian network can reasonably explain the behavior of the system and can express and reason knowledge in the presence of uncertain factors. Under the condition that complete information can be provided to a Bayesian network it provides accurate fault diagnosis, but in a single information event, or in the case where there are omissions or errors in the information, this method cannot accurately diagnose a fault. This method needs to quantify the probabilities of collected information, process the uncertain information, then through using pattern recognition, establish a correlation Bayesian network model to determine fault components and fault locations [5]. The fault diagnosis method of Analytic Models is mainly based on the switching information of power systems. The action and alarm information of the protection device are analyzed, and the optimal solution is solved by intelligent algorithm [7]. In the process of solving the model, there are multiple optimal solutions, and the most accurate diagnosis is difficult to obtain [8]. Information fusion is a new technology for fault diagnosis in recent years, and it is also an important development direction for the future. The main advantage of this method is that it can analyze power system faults from multiple angles and avoid diagnosis errors caused by a single point of information or information loss [9]. With the continuous improvement of big data technology, power system fault diagnosis based on multi-source information fusion has also achieved certain development [10]. In the case of complex faults of a power system, there are many uncertain factors, which lead to some errors in the above diagnosis methods. At present, the main countermeasures for the complex fault diagnosis of power systems still rely on the experience of operation and maintenance personnel. Manually determining the fault type and providing the fault solution greatly depends on previous knowledge.