[en] This paper discusses the application of machine learning to the design of power system blackout prediction criteria, using a large database 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 aime 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 the degree of anticipation and selectivity of detection rules. Tests are carried out on a a data base related to voltage collapse of an existing large scale power system.
Disciplines :
Computer science
Author, co-author :
Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Early prediction of electric power system blackouts by temporal machine learning
Publication date :
1998
Event name :
ICML-AAAI 98 Workshop on "Predicting the future: AI approaches to time series analysis"
Event place :
Madison, Wisconsin, United States
Event date :
July 24-26, 1998
Audience :
International
Main work title :
Proceedings of ICML-AAAI 98 Workshop on "Predicting the future: AI approaches to time series analysis"
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