Abstract
The electric power system is the largest artificial system so far. Many overhead transmission lines in the power grid are exposed to the natural environment for long periods, while the frequent occurrence of extreme weather events poses a severe threat to the power system. The data in this study came from multi-source dynamic data sets collected in real-time. These data are matched with previous overhead transmission lines disaster occurrences by coupling multiple to disaster factors through a decision support system based on predictive analysis. Then data mining techniques are used to establish the data models for transmission lines disaster to identify correlation models for factors such as transmission lines faults and meteorological data and to model transmission lines in HN Province, China, through analysis of disaster probability for prediction early warning. The results showed that the disaster model of overhead transmission lines matched the actual situation and had a high accuracy of early warning.
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