APPLICATION OF MACHINE LEARNING ALGORITHMS IN THE PROBLEMS OF IMPROVING MODE RELIABILITY OF MODERN POWER SYSTEMS
In order to increase the regime reliability of energy systems, the experience of applying machine learning algorithms and models for various issues of operative-dispatching and counter-accident management was reviewed. It is indicated that an effective solution to this problem is the use of machine learning algorithms and models that are able to learn to predict and control the operating modes of the power system, taking into account many changing influencing factors. The experience of using machine learning technology in the tasks of operational dispatch and emergency control of EPS is presented, which clearly shows the prospects of such studies for subsequent practical implementation in the work of various automated control systems for electric power networks of power systems. Until recently, models based on neural network structures have remained the most popular among machine approaches in predictive problems. The advantages of using this structure are shown, first of all, by the fact that the neural network structure makes it possible to obtain models with good approximating abilities. A comparative analysis of the effectiveness of various models in predicting electricity consumption is given. The issues of voltage and reactive power regulation in the electrical network of power systems using an artificial neural network are considered and the effectiveness of this approach is shown. A model and algorithm for estimating voltage stability in power system nodes under various influencing factors is proposed, as well as results are presented that confirm the reliability of the results obtained. © 2023, Gnedenko Forum. All rights reserved.
Библиографическая ссылка
Kurbatsky V. , Guliyev H. , Tomin N. , Ibrahimov F. , Huseynov N. APPLICATION OF MACHINE LEARNING ALGORITHMS IN THE PROBLEMS OF IMPROVING MODE RELIABILITY OF MODERN POWER SYSTEMS // Reliability: Theory and Applications. Vol.18. №4. 2023. P.716-728. DOI: 10.24412/1932-2321-2023-476-716-728
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