Machine learning in electric power systems adequacy assessment using Monte-Carlo method

This paper deals with the computational efficiency related problem appearing in electric power systems adequacy assessment using Monte-Carlo method. To attack this problem the novel method is suggested to reduce number of random states to be analyzed. The machine learning methods are employed for electric power system states precalculated classification. Random forest and support vector machine methods are proposed to use for solving this problem. Efficiency of proposed approach is demonstrated on test scheme.