АНАЛІЗ ЗАСТОСУВАННЯ ЗГОРТКОВИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ ПІДВИЩЕННЯ НАДІЙНОСТІ У РОБОТІ ФЕС

Abstract

This work is dedicated to the study of applying convolutional neural networks (CNN) for automatic recognition of photovoltaic panel conditions based on images. The focus is placed on the impact of CNN hyperparameter tuning, such as the number of layers, kernel size, learning rate, and activation function, on the accuracy of classi-fying various panel conditions, including physical damage, electrical defects, dust contamination, and clean pan-els. The use of CNNs is driven by their ability to automatically detect complex spatial patterns in images, which is crucial for accurately identifying different types of defects and anomalies on the panels. The main challenges include the variability in panel conditions and external factors, such as weather conditions, that affect data quali-ty. The influence of CNN hyperparameters on classification accuracy was analyzed, and their optimal values were determined to achieve high model accuracy. The relevance of the research lies in the growing role of CNNs in monitoring photovoltaic panel conditions, which enables timely defect detection and optimization of mainte-nance. The results demonstrate that proper hyperparameter tuning of CNNs significantly improves classification accuracy, contributing to increased efficiency and stability of photovoltaic stations. The paper emphasizes the importance of using neural networks for image analysis in the field of photovoltaic system maintenance, ensuring their effective integration into energy networks. The goal of the study is to improve the accuracy of photovoltaic panel condition classification by developing and tuning CNN models with optimal hyperparameters, enabling timely detection of panel defects and enhancing the efficiency of photovoltaic system maintenance.