Abstract: With the increasing scale of power grid, the massive amount of log information generated bydevices 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.
Zhu, Danlong; Yan, Yunqi; Chen, Ying; Zhang, Jiaqi; Jin, Longxing; and Fu, Wei
"Learning and Analysis of Dynamic Models for Grid Discrete Events Based on Log Information,"
Journal of System Simulation: Vol. 35:
10, Article 11.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss10/11
Zhu Danlong, Yan Yunqi, Chen Ying, et al. Learning and Analysis of Dynamic Models for Grid Discrete Events Based on Log Information[J]. Journal of System Simulation, 2023, 35(10): 2193-2201.
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