An event knowledge graph system for the operation and maintenance of power equipment

Abstract

Power equipment operation and maintenance (O&M) requires plenty of domain knowledge to improve equipment security and power grid reliability. However, most knowledge is implicitly represented by the semantic text, which is hard to be comprehended by algorithms and restricts the intelligence level of equipment O&M. Therefore, this article proposes an event knowledge graph system to automatically extract event knowledge from O&M reports and represent the knowledge by an event knowledge graph (KG). First, a bidirectional long short-term memory network (BiLSTM) combined with conditional random field (CRF) is employed to recognize the entities. Next, an attention-based sequence-to-sequence model is proposed to detect multiple events and extract documentlevel representation vectors. Then, a tree-based table filling strategy is utilized to complete the artificially designed event tables. Finally, the proposed system renders the event tables into an event KG. Verified by the experiments, the proposed system outperforms the baselines with the F-measure of 83.5% for event extraction and 94.2% for event detection. When dealing with coexisting events and scattering arguments, the F-measure of the proposed system achieves 74.8% and 79.0%, respectively. Moreover, the proposed system has positive effects on intelligent retrieval, fault diagnosis, and condition assessment, verifying its potential value for equipment O&M.

Knowledge Graph for Low Carbon Power and Energy Systems

“…Knowledge graph with the ability of statistical computation has also been also realized, with the goal of solving deep and complex logical reasoning problems, particularly for the task of event recognition. Tian et al [15] proposed an event knowledge graph with vectorial representation that allows knowledge automatically extracted from O&M reports and represented properly. This knowledge graph system made implicit semantic syntax of the power and energy system machine-interpretable and performs well in event extraction and detection.…”

Multi-objective Collaborative Optimization Method for Intelligent Distribution Network Operation and Maintenance

“…In this paper, firstly, it is essential to define the node type of active electrical power network, analyze power flow, and provide a basic guarantee for multi-target co-enhancement for complex environments operation [6][7][8][9][10]. The flow chart of multi-target co-enhancement research for intelligent electrical networks designed in this article is shown in Figure 1.…”

Intelligent substation virtual circuit verification method combining knowledge graph and deep learning

“…Currently, research on knowledge graph in the field of power is still in its nascent stage, with relevant literature mainly focusing on application exploration and macro framework design. Li and Wang (2023) proposed a multi-level, multi-category knowledge graph application framework for assisting decision-making in power grid fault handling and preliminarily elaborated on the key technologies and solution approaches within the framework; Tian et al (2022) utilized the graph structure of knowledge graph to express textual information and their relationships, extracting the information required to construct knowledge graph from operation and maintenance reports, realizing the automatic construction of knowledge graph, and proposing an automatic retrieval method for equipment operation and maintenance.…”