Calculation of the steady-state mode of the electric network using a genetic algorithm

Introduction. This article discusses the application of evolutionary optimization methods in problems of calculating the steady-state mode of an electrical network, in particular, genetic algorithms. Goal. Description of the population optimization method, which can be used to calculate the steady s...

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Published inВестник Северо-Кавказского федерального университета no. 1 (106); pp. 21 - 28
Main Authors Polischuk, V. I., Tkachenko, V. A., Shepelev, A. O.
Format Journal Article
LanguageEnglish
Russian
Published North Caucasus Federal University 01.03.2025
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Summary:Introduction. This article discusses the application of evolutionary optimization methods in problems of calculating the steady-state mode of an electrical network, in particular, genetic algorithms. Goal. Description of the population optimization method, which can be used to calculate the steady state of the electric network. Materials and methods. The object of the study is the population optimization algorithm. The subject of the study is the objective function of the genetic algorithm, which can be used to find voltage levels in the nodes of the electric network. The methods used in the work include an optimization method based on genetic algorithms, as well as the classical Newton method for calculating the steady-state mode of an electrical network. Results and discussion. The paper presents the main elements of a genetic algorithm for calculating the steady-state mode of an electrical network, defines an optimized target function, and presents the calculation results in comparison with Newton method. Conclusion. Based on the results of the conducted study, it can be concluded that the calculation of the steady state can be considered from the point of view of the optimization problem of power imbalances, the use of a genetic algorithm for calculating the electrical network mode is possible, but the accuracy of the results strongly depends on the number of iterations, although it does not require large computing power and complex calculations.
ISSN:2307-907X
DOI:10.37493/2307-907X.2025.1.2