SELECTION OF ARTIFICIAL NEURAL NETWORK PARAMETERS IN COMPLEX OPTIMIZATION FOR OPERATIONAL CONTROL OF POWER SYSTEMS

This article considers the processes of selecting and optimizing artificial neural network (ANN) parameters as a complex optimization method for operational control of power system states. The main goal of the study is to increase the efficiency of the power system in conditions of dynamic changes, ensure its stability and reliability. To achieve this goal, the structure of ANN models, learning algorithms, and methods for selecting hyperparameters were analyzed. Based on the results of experimental modeling and simulation, an optimal combination of parameters was determined, which significantly improved the system’s ability to make quick and accurate decisions. The results of the study provide approaches aimed at expanding the possibilities of effective use of ANNs in optimizing the state parameters of power systems.

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