# 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)

Table 3: Representation methods: comparison and evaluation. the text extraction. All of the above factors will affect the quality of extraction and the quality of the final KG. Nowadays, with the expansion of databases and the development of AI, LSTM-CRF [99], Bi-LSTM-CRF [100], LSTM-CNNs-CRF [101], and neural network models based on attention mechanisms [34] are widely used in the power domain. In equipment fault texts, the bi-directional LSTM model with CRF layer (Bi-LSTM-CRF) can fully extract semantic features in combination with the context: firstly, compared with neural network models, Bi-LSTM models can avoid the gradient explosion and gradient disappearance of RNNs; moreover, the CRF model can accomplish sequence labeling and avoid sequence drift. To compare the recognition effectiveness of the different models in power texts, Table 4 evaluates LSTM, Bi-LSTM, and Bi-LSTM-CRF in terms of three metrics: precision, recall, and the overall evaluation metric F1. It proves that the Bi-LSTM-CRF model can identify keywords for business operations with high accuracy, fully extract the information features of text passages, and enhance the fast retrieval and recommendation capability of the grid. [PERSON]. [102] showed the recognition results of CRF and Bi-LSTM models. Compared with Bi-LSTM, the CRF model has an accuracy of 83% for entity extraction, which is more suitable for entity extraction in the power domain KG. Considering the influence of textual information around the numbers on the training of numerical vectors, [PERSON]. [103] proposed an optimization model based on the Bi-LSTM-CRF. The new model can identify the information of faulty text more accurately. Additionally, the F-value is improved by 5.6% compared to the base model. KG can also cooperate with a variety of technologies for power equipment O&M. In the error checking of defective texts, merging and sorting rules can be formulated for the difficult problem of word ambiguity to improve the accuracy of word separation. Intelligent query systems for electrical equipment can incorporate Q&A sessions into the O&M of electrical equipment: first, a pure Bayesian classifier can segment and feature word classification of user questions; then, relevant knowledge is retrieved from the Neo4 database for Q&A matching based on the extracted features to provide solutions and preventive measures. Moreover, the combination of power IoT and KG technology is the solution to problems such as the continuous increase in real-time data and complex operating situations. The graph structure form of KG allows for the organic linking of interacting devices in a graph. We can use the KG technology to achieve holographic awareness of data for the power IoT and to perform intelligent operation management [104] and fault maintenance [98] of power equipment: for example, [105] integrated multiple sources of data in power end devices in the power IoT to achieve holographic sensing of all power devices, allowing real-time knowledge of device conditions.