Reference : Early prediction of electric power system blackouts by temporal machine learning
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/25759
Early prediction of electric power system blackouts by temporal machine learning
English
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
1998
Proceedings of ICML-AAAI 98 Workshop on "Predicting the future: AI approaches to time series analysis"
21-27
Yes
No
International
Madison (Wisconsin)
ICML-AAAI 98 Workshop on "Predicting the future: AI approaches to time series analysis"
July 24-26, 1998
Madison, Wisconsin
USA
[en] power systems ; machine learning
[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.
http://hdl.handle.net/2268/25759
http://www.montefiore.ulg.ac.be/services/stochastic/pubs/1998/GW98

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