Volume 11, no. 4Pages 146 - 153

Machine Learning in Electric Power Systems Adequacy Assessment Using Monte-Carlo Method

D.A. Boyarkin, D.S. Krupenev, D.V. Iakubovskii
The article considers the question of increasing the computational efficiency of
the procedure for electric power systems adequacy assessment using the Monte Carlo method.
In the framework of using this method, it is necessary to randomly generate a certain number
of system states. As it is known the speed and accuracy of the calculation depends on the number
of such states to be analyzed, so one of the ways to solve this problem is to reduce the this
number while observing the required accuracy of the estimate. For this purpose it is proposed
to use machine learning methods, whose task is to classify the calculated states of the electric
power system. During the experiment, the support vector machines method and the random forest
method were applied. The results of the calculations showed that these methods using allowed to
reduce the number of random states of the system to be analyzed, thereby reducing the total time
spent on calculations in general and proving the effectiveness of the proposed approach. Wherein
the best results were obtained while using the random forest method.
Full text
electric power systems; adequacy assessment; Monte Carlo method; machine learning.
1. Kovalev G.F., Lebedeva L.M. Nadeshnost' sistem elektroenergetiki [Electric Power Systems Reliability]. Novosibirsk, Nauka, 2015. (in Russian)
2. Panasetsky D.A., Tomin N.V., Voropai N.I. Development of Software for Modelling Decentralized Intelligent Systems for Security Monitoring and Control in Power Systems. IEEE Eindhoven PowerTech, 2015, pp. 1-6.
3. Vapnik V.N. Chervonenkis A.Y. Teoria raspoznavaniya obrazov [Theory of Pattern Recognition]. Moscow, Nauka, 1974. (in Russian)
4. Breiman L. Random Forests. Machine Learning, 2001, vol. 45, no. 1, pp. 5-32.
5. Knuth D. The Art of Computer Programming. Vol. 2. Seminumerical Algorithms. Addison-Wesley, 1981.
6. Matsumoto M., Nishimura T. Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudorandom Number Generator. ACM Transactions on Modeling and Computer Simulations, 1998, vol. 8, pp. 3-30.
7. Sobol I.M. Mnogomernye kvadraturnye formy i funktsii Haara [Multidimensional Quadrature Formulas and Haar Functions]. Moscow, Nauka, 1969. (in Russian)
8. Levin A.A., Chistyakov V.F., Tairov E.A. On Application of the Structure of the Nonlinear Equations System, Describing Hydraulic Circuits of Power Plants, in Computations. Bulletin of the South Ural State University. Series: Mathematical Modelling, Programming and Computer Software, 2016, vol. 9, no. 4, pp. 53-62. DOI: 10.14529/mmp160405
9. Krupenev D.S., Boyarkin D.A., Yakubovskiy D.V. [Generation of Random States of Electric Power Systems at Assessment of Their Reliability by the Monte Carlo Method]. Safety and Reliability of Power Industry, 2017, no. 10, pp. 33-41. (in Russian)
10. Van Rijsbergen C.J. Information Retrieval. London, Butterworths, 1979.