Research on the damage class prediction method in typhoon disaster for power system

Abstract

Power system is one of the most fundamental infrastructure systems for human society. The stability of power system greatly affects the economy and security of the city. Therefore, recovering the power system in cities after a severe disaster is the high priority work in a disaster emergency process. Among several kinds of huge disasters, typhoon is a highly valuable research target. Different from the earthquake or tsunami, typhoons often last several days before landing after their generation, so there is much more time for people to prepare and cope with the damage to power system. In this paper, a damage class prediction method for determining the command institution is researched. Based on the actual power system damage data in China from 2004 to 2019, this research proposed the prediction method with machine learning algorithm. The result shows SVM model can reach 77.4 % accuracy in this task, and it is accurate enough to support the emergency decision.