# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 1 Introduction
### Power System Fault Processing Technology Based on Deep Learning Technology  
In recent years, with the rapid development of computer technology, the research tide of deep learning technology has been rising. Deep learning technology can process large amounts of data and extract deep features of input data. It has been applied to healthcare, image recognition and natural language processing with great success [11]. The application of deep learning technology in fault diagnosis can realize the combination of feature extraction and classification, and has less dependence on professional knowledge. Deep learning is widely used in power systems, such as power consumption prediction, wind and solar energy prediction, power disturbance detection and classification, fault detection, energy management and energy optimization [12; 13; 14]. Deep learning technology has strong abilities of data feature extraction and provides a new research direction for power system fault diagnosis [15]. In 2012, Google took the lead in introducing the concept of knowledge graphs. As a special knowledge base, a knowledge graph extracts entities and relationships from unstructured knowledge to form relationships, and stores these relationships in the form of a directed graph, which can express unstructured knowledge in a standardized way. A knowledge graph can be regarded as a semantic map composed of several nodes and edges. Each entity or concept is a node in the knowledge graph [16]. The concept is the abstraction of a certain kind of phenomenon or transaction, such as the system Operation and Maintenance personnel; an entity is a specific person or thing, such as the specific name of the system operation and maintenance personnel. The edge connecting two nodes represents a certain relationship between the nodes. Among many different knowledge representation methods, the two characteristics of knowledge graphs are particularly prominent, that is, strong expressive ability and ease of extension. Utilizing the powerful expressive ability of a knowledge graph and its rich relationship information, then combining it with the relationship reasoning rules in the scenario of power system production, operation and maintenance, the log data can be mined to complete various reasoning applications, such as inconsistency detection, inference completion, knowledge discovery, auxiliary reasoning, and decision-making. Knowledge graphs can structure the heterogeneous knowledge and construct the correlation between pieces of knowledge. They can be used to solve the diversification, complexity and islanding of fault data, and standardize the storage of fault knowledge for fault diagnosis. In addition, the knowledge graph also has the advantage of interpretability, which can provide a reasonable explanation for the fault causes and solutions. At present, knowledge graph technology is not widely used in the field of fault diagnosis. [PERSON] et al. [17] studied fault treatment based on power information knowledge graphs and proposed a fault diagnosis method. A BiLSTM-CRF model was used to conduct entity recognition of power domain knowledge and realize the construction and application of a power grid domain knowledge graph. [PERSON] et al. [18] proposed an intelligent question answering system for fault diagnosis of power information acquisition systems based on knowledge graph technology, which can efficiently traverse searching of nodes and paths thus significantly improve reasoning efficiency. [PERSON] et al. [19] proposed the BERT-BiLSTM-CRF model to extract knowledge from Chinese technical literature and construct a knowledge graph of electric power equipment faults. In power equipment fault defect text, there are many entities related to equipment fault information, with various categories and large differences in features. It is difficult to extract all entities in the text by using a single dictionary or machine learning method, some entity values are expandable, so there is no directly applied extraction method.