# 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
#### 3.2.1 Power Equipment Operation and Maintenance (O&M)  
Highly invested power equipment requires constant inspection and maintenance to enhance its service life and meet increasing load demands. Traditional O&M of power equipment uses standards based on expert experience and requires operators to make manual judgments about faults. However, it is not accurate to report abnormal situations in the power system. In the era of big data, considering the complexity and variability of power grids, a series of self-learning methods combined with algorithms and predictive models can flexibly develop O&M plans [96]. Especially, the representation of KG in equipment O&M has unique advantages and is still worth studying. Ref. [97] compared several representations of defective texts for electrical equipment. As shown in Table 3, compared to several other representation methods, the KG representation method had the best results for the three indicators of precision, recall, and F1-score. This also explains that it is necessary to represent the power equipment data with knowledge graphs. Figure 3: Power system operation application scenarios of the KG. Long-running power equipment accumulates a large amount of text information such as O&M logs and power inspection work orders. Based on the textual data in logs and sensor data, the knowledge data structure can organically relate the interacting devices to the KG. Therefore, we could combine these unstructured data with industry-standard documents and equipment online monitoring data to build a power equipment O&M decision KG. In this way, the KG can realize the data holographic perception of power IoT and carry out intelligent operation management and fault maintenance [98]. The use of KG technology to assist in the O&M of power equipment can be summarized in four steps: 1 Building a fault corpus and semantic model for text and complex scenarios in power equipment. 2 Developing data cleaning modules for error identification and quality improvement of texts. 3 Automatically constructing a large power equipment KG and a fault pattern KB. 4 Assisting with appropriate machine learning methods for specific problems. Using the case-based reasoning approach in Section 3.1.3, KG technology can provide fault-assisted decisions. For a new defect record, such as transformer oil leak, the case-based reasoning approach can look for similar paths in the KG. By applying the idea of the path ranking algorithm (PRA) to these paths for similarity ranking, KG can provide a new decision based on previous processing methods. This process enables reasoning and prediction of entities and their relationships, providing the user with answers and an intuitive analysis process. As shown in Figure 4, when an operator inputs a fault, KG first obtains the input entities and relationships through extraction techniques, then matches the extracted content with the fault information corpus and the expert experience KB by path matching, and finally selects the case with the highest path matching ranking to the operator as the matching maintenance plan. Therefore, the application of KG improves the intelligence and efficiency of power equipment operation and management and is important for practical decisions on defect handling. The named entity recognition (NER) in the fault text is a major difficulty in the equipment O&M KG construction. The subjectivity of the O&M logs, the inconsistency of the recording methods and text structure, and the deviation in the way to describe the same fault all cause trouble for knowledge extraction. Meanwhile, the same entity has multiple names. Additionally, some special characters may be accidentally lost in  
Figure 4: The KG technology in power equipment O&M. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Representation Method** & **Precision/\%** & **Recall/\%** & **F1-Score/\%** \\ \hline KG & 93.62 & 92.77 & 93.19 \\ VSM with tf-idf & 50.94 & 69.18 & 58.68 \\ LSI & 44.81 & 63.68 & 52.60 \\ LDA & 47.59 & 63.85 & 54.53 \\ \hline \hline \end{tabular}
\end{table}