Optimization and learning of load restoration strategies
This paper describes an application of optimization and machine learning to load restoration in a generationtransmission 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.
restoration machine learning genetic algorithms simulation tools