# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
###### Abstract
As a new project of AI in power systems, KG is expected to become the core technology of next-generation power systems by shifting the vision from data-centered to knowledge-centered. KG takes the triadic form of entity, relationship, and entity as the basic constituent unit and integrates multi-source heterogeneous data into a logical graph through a series of extraction and fusion techniques. At present, the applications of KG are broadly divided into two categories: general knowledge graph (KG) applications and domain knowledge graph (DKG) applications. For the generic domain aspect, the more common research is semantic search [7]. Semantic search has not only significantly improved the accuracy and predictability of search engines such as Google and Baidu but also injected new vitality into industries such as cloud resource scheduling [8]. For domain-specific aspects, the application of KG in many fields is still not perfect. KG has only been established in a relatively small number of fields, and the more representative ones are medicine [9; 10], finance [11; 12], and computer technology [13; 14]. Although the application of KG in specific industry fields has only just started, it can be found that the introduction of KG technology has brought great convenience and practicality to the development of various industries, and has overcome many technical difficulties that could not be resolved before. So, why should KG be applied to electric power systems? Referring to examples of successful applications in other fields, we analyze four main characteristics of power systems. 1 Complexity. As a complex nonlinear operating system, the model of a super-scale power system is complex. 2 Multiplicity. Due to the coupling relationship between the power system and multiple domains, the massive data in the power grid have complex sources and different types. 3 Real-time. The power system needs to change in real-time to meet the increasing load demand on the customer side. 4 Security. Unlike other fields, the power industry requires a very high level of risk control. In more cases, expert experience is often more reliable than AI algorithms. Based on the above characteristics of the power system, KG technologies are integrated into the power system. The form of the KG graph structure fits with the topology of the power system and can visualize the complex models in the power grid. Knowledge extraction and fusion technology can effectively integrate the massive data in the power IoT center and make full use of data resources. The whole power system is correlated by KG to synchronize the control and adjustment of multiple subsystems. The time-series dynamic KG [15] can realize the simultaneous update of KG and real-time change system to cope with the real-time change in power flow. In addition, the graph structure helps the diagnosis and analysis of events. Its reasoning process is traceable throughout, which makes up for the shortcomings of the uninterpretable neural network model. In this paper, we provide a systematic review of current research on KG in power systems. We will pose the following research questions. 1. What are the key technologies and necessary processes to build a KG? How to construct a KG in the power domain? 2. Compared with other fields, what are the technical difficulties of KG in power systems? How are they solved? 3. What is the progress of current research on KG, and in which areas are KGs more used in power systems? Which areas are less used? 4. What is the future direction of research on KG in the field of electric power? To answer these questions, the rest of the paper is organized as follows: Section 2 outlines the key technologies and necessary processes for building a KG. Section 3 discusses the application of KG in power systems. Among them, Section 3.1 introduces the construction method of DKG based on the characteristics of the power system. Section 3.2 describes the current popular research scenarios as well as their technical difficulties.