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

Статья конференции

Boyarkin D.., Krupenev D.S., Iakubovskiy D.., Sidorov D.N.

2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017

Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017. ID: 8109871. P.201-205.

DOI: 10.1109/SIBIRCON.2017.8109871

ISBN: 9781538615966

2017

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. © 2017 IEEE.

Библиографическая ссылка

Boyarkin D.., Krupenev D.S., Iakubovskiy D.., Sidorov D.N. Machine learning in electric power systems adequacy assessment using Monte-Carlo method // Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017. ID: 8109871. P.201-205. ISBN (print): 9781538615966. DOI: 10.1109/SIBIRCON.2017.8109871

adequacy assessment