Visual State Estimation for False Data Injection Detection of Solar Power Generation

As the penetration level of solar power generation increases in smart cities and microgrids, an automatic energy management system (EMS) without human supervision is most communly deployed. Therefore, assuring safe and reliable data against cyber attacks such as false data injection attacks (FDIAs) has become of utmost importance. To address the aforementioned problem, this paper proposes detecting FDIAs considering visual data. The aim of visual state estimation is to enhance the resilience and security of renewable energy systems. This approach provides an additional layer of defense against cyber attacks, ensuring the integrity and reliability of solar power generation data and facilitating the efficient and secure operation of EMS. The proposed approach uses a modified VGG-16 neural network model to obtain an intermediate representation that provides textual and numerical explanations about the visual weather conditions from sky images. Numerical results and simulations corroborate the validity of our proposed approach. The performance of the modified VGG-16 neural network model is also compared with previous state-of-the-art machine learning models in terms of accuracy.