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research article
Optimization and learning of load restoration strategies
Kostic, Tatjana
•
Germond, Alain J.
•
Alba, Juan J.
This paper describes an application of optimization and machine learning to load restoration in a generation-transmission system. An optimization procedure, combining a genetic algorithm and a power system dynamic simulator, generates the appropriate sequence of operations for each state of the power system. A machine learning technique (induction of decision trees) is applied to extract decision criteria that will guide the load restoration after a generalized black-out. The paper also presents the results of applying these techniques to a power system of realistic size.
Type
research article
Authors
Kostic, Tatjana
•
Germond, Alain J.
•
Alba, Juan J.
Publication date
1998
Volume
20
Issue
2
Start page
131
End page
140
Peer reviewed
REVIEWED
EPFL units
Available on Infoscience
April 4, 2007
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