Keywords :
electric power generation; frequency control; learning (artificial intelligence); power engineering computing; support vector machines; time seriesBelgian system; ancillary services; generator behaviour; primary frequency control behaviour; supervised automatic learning; support-vector machines; time-series
Abstract :
[en] In this paper we propose a methodology based on supervised automatic learning in order to classify the behaviour of generators in terms of their performance in providing primary frequency control ancillary services. The problem is posed as a time-series classification problem, and handled by using state-of- the-art supervised learning methods such as ensembles of decision trees and support-vector machines combined with several preprocessing techniques. The method was designed in the context of the Belgian system and is validated on real-life data composed of more than 600 time-series recorded on this system.
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