Xoliddinov_Begmatova_NMTI

Abstract

This study presents a detailed development and analysis of a photovoltaic system model integrated with a rechargeable battery and equipped with an advanced fuzzy controller based on a DC-DC converter. The key element of the model is an intelligent control system based on the principles of fuzzy logic and implemented using the adaptive neuro-fuzzy inference Systems (ANFIS) toolkit. This approach has made it possible to create a flexible and adaptive management system capable of responding effectively to changing environmental conditions. The model takes into account a wide range of input parameters, including time of day, ambient temperature, and other significant factors affecting the performance of a photovoltaic system. Based on this data, the system predicts the output voltage and optimizes the charging/discharging process of the battery. During the design of the model, the system was thoroughly trained in order to form a knowledge base that includes a set of vague rules and conditions. This made it possible to achieve high modeling accuracy and an adequate reflection of the real behavior of the photovoltaic system. The simulation results demonstrate the effectiveness of the proposed approach and open up new prospects for optimizing the operation of photovoltaic systems, increasing their reliability and reducing operating costs.