Early Prediction of Electric Power System Blackouts by Temporal Machine Learning

P. Geurts and L. Wehenkel

This paper discusses the application of machine learning to the design of power system blackout prediction criteria, using a large data base of random power system scenarios generated by Monte-Carlo simulation. Each scenario is described by temporal variables and sequences of events describing the dynamics of the system as it might be observed from real-time measurements. The aim is to exploit the data base in order to derive as simple as possible rules which would allow to detect an incipient blackout early enough to prevent or mitigate it. We propose a novel "temporal tree induction" algorithm in order to exploit temporal attributes and reach a compromise between degree of anticipation and selectivity of detection rules. Tests are carried out on a data base related to voltage collapse of an existing large scale power system.

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