https://doi.org/10.36719/2789-6919/42/165-174
Ilkhombek Kholiddinov
Ferghana Polytechnic Institute
Doctor of Technical Sciences
https://orcid.org/0000-0002-0120-4043
i.xoliddinov@ferpi.uz
Mukhlisakhon Begmatova
Ferghana Polytechnic Institute
Doctoral student
https://orcid.org/0000-0003-4405-4943
muxlisaxonbegmatova1991@gmail.com
Development of a Fuzzy Regulator for Load Balancing Using Solar Panels in Low-voltage Electrical Networks
Abstract
This article presents a simulation of a photovoltaic system with a rechargeable battery equipped with a bidirectional direct current to direct current (DC-DC) converter that provides efficient energy management. An intelligent control model based on an adaptive neuro-fuzzy inference system (ANFIS) has been developed, which integrates the capabilities of neural networks and fuzzy logic to optimize the system. The model takes into account key input parameters such as time of day, solar radiation intensity, and ambient temperature to accurately predict output voltage and power regulation.
At the design stage, the system was trained with the formation of fuzzy rules and conditions, which made it possible to achieve high accuracy in reproducing the behavior of the system in various operational scenarios. The results obtained demonstrate the stability of the model’s operation under changing external conditions, as well as its ability to adapt to dynamic load changes.
The analysis shows that the use of the ANFIS algorithm in the management of a photovoltaic system provides increased energy efficiency, improves load balancing and reduces the impact on the low voltage network (0.4 kV). The proposed approach can be used to integrate renewable energy sources into electrical networks, ensuring the stability and reliability of their operation.
Keywords: electricity, network, voltage, solar panels, efficient energy management