Journal of System Simulation ›› 2023, Vol. 35 ›› Issue (10): 2193-2201.doi: 10.16182/j.issn1004731x.joss.23-FZ0800

• Papers • Previous Articles     Next Articles

Learning and Analysis of Dynamic Models for Grid Discrete Events Based on Log Information

Zhu Danlong1(), Yan Yunqi2, Chen Ying2(), Zhang Jiaqi2, Jin Longxing1, Fu Wei1   

  1. 1.Shenzhen Power Grid Utility Co. LTD, Shenzhen 518048, China
    2.Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
  • Received:2023-07-02 Revised:2023-09-12 Online:2023-10-30 Published:2023-10-26
  • Contact: Chen Ying E-mail:zhudanlong@sz.csg.cn;chen_ying@tsinghua.edu.cn

Abstract:

With the increasing scale of power grid, the massive amount of log information generated by devices in the power grid poses a challenge to the manual analysis of abnormal grid conditions. The log information generated during the operation of the power grid has the typical discrete sequential characteristics. By analyzing the log information of grid alarm messages, a station event transition probability model and an event sequence risk calculation method are proposed to effectively model and analyze the abnormal operation level of primary and secondary systems in substations. The proposed method not only successfully identifies the event sequences corresponding to grid failures that have been manually recarded by dispatchers but also identifies the abnormal alarm information sequences that have not been recorded by dispatchers. This helps analyze and assess the operational situation of power grid equipment, discover potential risks, and improve the efficiency of substation maintenance.

Key words: power system, alarm information, dynamic feature learning, anomaly detection, risk assessment

CLC Number: