# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
## 3 Applications of Knowledge Graphs in the Power System Operation
### Power System Operation Application Scenarios for the KG
#### 3.2.3 Scheduling Fault Decisions  
As the power system has expanded and become more complex, the dispatch fault decision mechanism has undergone a series of changes. Initially, power dispatch faults required manual decisions by operators based on personal experience [120]. Then, grid companies used dispatch expert systems to determine fault problems [121]. However, scheduling faults are never able to be detected precisely. At the same time, the past methods made it difficult to diagnose faults quickly and could not automatically provide appropriate solutions. As a guarantee for the continuous and stable operation of the power system, the dispatching link needs new technologies to automatically diagnose faults. The powerful knowledge reasoning and graphical format of KG technology are capable of mining the latent relationships between entities and giving precise and targeted decision solutions for faults present in line transmission. The key steps to realize automatic decision-making on power dispatch faults are as follows. First, obtain a large amount of scheduling fault information, such as scheduling protocols, expert experience, and fault cases. Then, extract a large amount of dispatching terminology from the semantic text and combined it with the structured operation data of the power grid to build a corpus of terminology and faults. Eventually, a large number of dispatching rules in the semantic library are learned and reasoned [122] to build a KB exclusively for dispatching decisions. Once a fault occurs, KG technology can perform knowledge search and reasoning, mobilize the semantic and knowledge bases for the corresponding scenario, and automatically discover fault conditions such as overload, high-power loss, and voltage deviation. Based on the fault content, the corresponding control objects and operating procedures are provided from the fault decision KB [123]. The introduction of KG provides new ideas for dispatch fault handling. The business logic KG constructed by combining bottom-up and top-down approaches helps us perceive the business relationships of the dispatching system and assists operators in performing auxiliary fault analysis in the case of system faults. As the software part of a system, KG technology can search for grid equipment KG according to grid regulation and control requirements and generate new dispatching decision solutions in a shorter time. Intelligent dispatching with KG-assisted decision-making can solve the problems of long dispatching time and low dispatching frequency of the current dispatching system. Based on the integrated ontology KB, a decision support system for emergency dispatch of power systems was developed in [124]. The system enables knowledge extraction for online control of power systems in emergencies and automates intelligent emergency dispatching. In addition to this, the combination of neural network models and various algorithms enables a shift from traditional dispatching to smart grid dispatching. For example, GCN [125] can automatically learn any graphical feature. Ref. [126] used GCN to read features from the scheduling domain KG. The knowledge representation and inference of the read features provide decision support for risk management, load balancing, and system optimization in scheduling with reduced scheduling errors. Although KG research in the field of power grid dispatching accounts for the majority of the KG in power systems, most of the current research is focused on dispatching fault decision-making [127]. In addition to fault diagnosis, many other aspects of power dispatch lend themselves to fast and accurate problem-solving in the form of graph structures. We need more in-depth and systematic research on real-time monitoring and warning, safety inspection, dispatch planning, and management [128]. Scheduling has many risks in practice and requires the integration of multi-domain knowledge to improve the safety of the grid and the accuracy of decisions.