Research Status and Prospect of Deep Learning in Secondary State Monitoring of Smart Substation

摘要

The network of the secondary system of the smart substation realizes the sharing and interaction of equipment information, and also brings a large amount of secondary system status data. Conventional secondary condition monitoring methods have deficiencies in processing big data. As a research hotspot of artificial intelligence, deep learning has strong data mining capability that meets the needs of state monitoring of smart substation secondary systems. In this context, the paper first outlines the basic ideas of deep learning and the typical structure of several commonly used models. Secondly, the concept, monitoring object and index selection of secondary system monitoring in smart substation are discussed. The advantages and disadvantages of using conventional methods and deep learning in communication network condition monitoring and secondary device status evaluation are then analyzed. Finally, combined with the current research and application status of deep monitoring of secondary system status in smart substation, the future development prospects are prospected.