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

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.

Keywords

electric power systems; adequacy assessment; Monte Carlo method; machine learning.

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