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.