Intelligent control of energy system operating modes based on  neuro-analytical and neutrosophic models under conditions of  uncertainty

Oksana Porubay; Isamiddin Siddikov; Dilnoza Umurzakova

Authors

Oksana Porubay Department of Software Engineering and Cybersecurity, Faculty of Information Technology and Telecommunications, Fergana State Technical University, Fergana, Uzbekistan;
Isamiddin Siddikov Department of Information Processing and Management System, Faculty of Electronics and Automation, Tashkent State Technical University named after Islam Karimov, Tashkent, Uzbekistan
Dilnoza Umurzakova Department of Computer Engineering and Artificial Intelligence, Faculty of Information Technology and Telecommunications, Fergana State Technical University, Fergana, Uzbekistan

Keywords:

Neuro-analytical network; Neutrosophic logic; Intelligent control; Smart Grid; Energy system optimization; Uncertainty modeling; Hybrid learning algorithms.

Abstract

This paper introduces a novel approach to intelligent control of Electric Power System
(EPS) operating modes under uncertainty, integrating Smart Grid technologies, a Neuro-Analytical
Network (NAN), and neutrosophic logic. A modified NAN architecture with an additional static
layer is proposed, enabling higher accuracy and faster response in real-time energy flow
management. To enhance resilience and adaptability in the presence of structural, parametric, and
informational uncertainties, neutrosophic logic is applied to model contradictory and partially
undefined input data. A hybrid learning methodology is developed, combining backpropagation
with the least squares method to ensure efficient adaptation using both historical and real-time
datasets. The proposed neuro-neutrosophic control system demonstrates the ability to mitigate
disturbances, reduce energy losses by up to 1%, stabilize voltage, and minimize phase imbalances.
A software platform has also been designed to implement the proposed algorithms, providing
automated analysis, forecasting, and control of EPS operating modes under uncertainty. The
system features a user-friendly interface and supports operator decision-making. The main
contribution of this study lies in developing an integrated framework that combines
neuro-analytical modeling, neutrosophic reasoning, and hybrid training techniques to improve the
robustness, efficiency, and reliability of power system operation in uncertain environments.