NODAL LOAD MODELS IN THE PROBLEMS OF EVALUATING AND FORECASTING THE RISK OF EMERGENCY SITUATIONS IN ELECTRICAL ENERGY SYSTEMS

• Date: 2022
• Author Affiliations: E. I. Bardyk — Institute of Electrodynamics of the National Academy of Sciences of Ukraine, Kyiv, Institute of Renewable Energy National Academy of Sci-ences of Ukraine, Kyiv; Y. S. Koval — National Technical University of Ukraine "Ihor Sikorsky Kyiv Polytechnic Institute", Kyiv
• Keywords: keywords
• Main Authors: Bardyk, E. I., Koval, Y. S.
• Format: Article
• Language: Ukrainian
• Published: Institute of Renewable Energy National Academy of Sciences of Ukraine 2022
• Subjects: Load forecasting; EES subsystem; ARIMA; ARMAX; GMDH.
• Online Access: https://ve.org.ua/index.php/journal/article/view/369
• Journal Title: Vidnovluvana energetika

The conditions of operation of modern electric power systems (EES) with power plants of various types are analyzed. The need to increase the accuracy of forecasting the service life of electrical equipment and nodal load of power systems has been established. For the tasks of analyzing the risk of emergency situations in the UES in case of failures of electrical equipment, a software module for processing retrospective and operational information on the values of the node load has been created. An approach to the transformation of statistical probability distributions into fuzzy intervals is proposed, which makes it possible to use the obtained dependencies with different forms of representation of the initial information in probabilistic-statistical modeling of UES modes. A mathematical model of nodal load forecasting based on MGUA-like neural networks is built.