@article{article_1656368, title={Detection of Cyberattacks on Photovoltaic Systems in Smart Grid Infrastructure Using Machine Learning Methods}, journal={Turkish Journal of Science and Technology}, volume={20}, pages={445–454}, year={2025}, DOI={10.55525/tjst.1656368}, author={Sakkar, Usame and Tatar, Ayşe Kübra}, keywords={Cyber-attack, distributed energy resources, machine learning, PV generation, smart grids}, abstract={With the increasing concerns over carbon emissions and environmental sustainability, the share of renewable energy sources in power systems has been steadily rising. These systems, which generate variable power depending on meteorological conditions, cause fluctuations in the energy supply-demand balance. Such fluctuations can only be effectively managed through smart grid infrastructure. While smart grids necessitate the integration of communication and information technologies, they also transform power systems into cyber-physical structures, introducing new cybersecurity risks. The integration of distributed generation sources into power systems brings additional cybersecurity threats. Among these threats, false data injection attacks (FDIA) pose significant risks by misleading state estimators (SE), potentially creating severe security vulnerabilities and operational risks. In this study, cyberattacks aiming to manipulate the energy supplied to the grid from photovoltaic (PV) panels and to deceive smartmeter data were analyzed using machine learning-based binary classification methods. The variations in generation levels under low, medium, and high-intensity cyberattack scenarios were modeled using widely adopted algorithms in the literature, including Random Forest Classifier (RFC), XGBoost Classifier (XGBC), and Gradient Boosting Classifier (GBC). The models achieved high accuracy rates, with 92.33% obtained from XGBC in the low-severity attack scenario and 68.59% from GBC in the high-severity attack scenario.}, number={2}, publisher={Fırat University}