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

Abstract

This article considers the processes of
selecting and optimizing artifcial 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 effciency 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 analyze






Based on the results of experimental modeling
and simulation, an optimal combination of
parameters was determined, which signifcantly
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