Optimization and learning of load restoration strategies

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 generalised black-out. The paper also presents the results of applying these techniques to a power system of realistic size


Published in:
PSCC. Proceedings of the Twelfth Power Systems Computation Conference, vol.1, 276 - 84
Year:
1996
Keywords:
Note:
load restoration strategies optimisation;load restoration strategies learning;machine learning;generation-transmission system;genetic algorithm;power system dynamic simulator;operations sequence generation;decision criteria extraction;generalised black-out;
Laboratories:




 Record created 2007-04-04, last modified 2018-03-18


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