Research on safety measures for secondary maintenance of Intelligent Substation

There are many problems in the secondary maintenance of intelligent substation, such as the long time of checking SCD documents;the unclear mutual influence of operation process interval, etc. Especially in the interval expansion, the above problems are obvious. This paper uses graph signal processing technology to decompose station information monitoring model into SCD static configuration model and dynamic timing model. For the static model, machine learning is used to automatically compare the incremental point table to realize the automatic maintenance function of the station endpoint table;for the dynamic time series model, a prefabricated rule safety measure sequence reference database is established, and self-learning fuzzy algorithm is used to realize the self-learning of safety measure template. Finally, the paper builds an online monitoring platform for safety measures, uses the technology of state perception and real-time scene monitoring to dynamically track the implementation of safety measures and prevent misoperation. The effectiveness and practicability of the proposed safety measures for secondary maintenance are proved by engineering examples and applications.