Abstract: In the initial construction process of smart grid dispatching control system in power grid dispatching control center, because different subsystems are in decentralized development, independent operation and independent management, it is easy to reduce data interconnection, which leads to difficulties in data sharing and restricts the information level of the system. The data is multi-source, and the data format is inconsistent, resulting in the application problems that the data can not be shared, accessed, managed, analyzed and mined in real time among different subsystems. In order to solve the problems of data sharing and mining, this paper constructs a knowledge map entity extraction model to study the power grid fault events. Based on the knowledge map theory, the structured and unstructured data related to power grid dispatching are processed to improve the application efficiency of data. Cleaning the preprocessed data to obtain the corresponding entity value and attribute value. The knowledge extraction model of power grid fault event reasoning knowledge mapping is constructed, and the power grid fault event reasoning knowledge edge mapping system is designed to extract the relationship between events and complete data storage. The experimental results show that the text prediction degree of the proposed model is high, which can reach more than 95; The accuracy is 96.71%, the recall rate is 94.88%, and the F1 value is 9.27%. This proves the feasibility of this study, in order to provide data and theoretical support for intelligent management and real-time dispatching of power grid.
Keywords: Power grid fault, event
reasoning, knowledge map, data extraction, data mining
