A Power Grid Fault Diagnosis Method Considering Online Dynamic Alarm Information

Abstract

With the continuous development of the smart grid and the gradual improvement of the demand for grid reliability and power grid self-healing capability, a large number of artificial intelligence technologies have been applied to the power grid. In order to improve the automation of the power grid, realize automatic fault identification and diagnosis after a power grid failure, and improve the efficiency of fault handling by the dispatching department, it is of great practical significance to use alarm information to achieve accurate fault judgment. However, the difference between the dynamic reading of alarm information flow and static samples in engineering applications is often ignored in the research of fault diagnosis using alarm information, which makes it difficult to apply fault diagnosis methods in engineering practice. Based on the characteristics of the LSTM algorithm, such as deep knowledge extraction ability, data mining ability and time series knowledge processing ability, this paper studies the power grid fault diagnosis method based on LSTM to adapt to the data of each sampling time window in the process of online fault diagnosis, solves the problems faced by the application of traditional diagnosis methods in engineering practice, and effectively improves the reliability of fault diagnosis.