Detecting False Data Injection Attacks (FDIAs) in Power Systems Based on Entropy Criteria

Load redistribution (LR) attacks in power systems can cause significant damage to the power grids, which leads to blackouts and other disastrous consequences. The paper aims to detect LR attacks using entropy-based features, even with limited information, to provide a practical solution. The paper presents an entropy-based method for LR attack detection, which is superior to traditional methods in identifying abnormal system behavior. The proposed method uses entropy to extract features that can differentiate normal and abnormal system behavior. The probability density function (PDF) of LR attacks is used to calculate the entropy of the system, which can then be used as a feature for detection. The paper concludes that the entropy-based approach offers a practical and effective solution for detecting LR attacks, even with limited information. The proposed method is a model-based free approach, making it highly desirable for practical applications. The results obtained on the IEEE 14-bus system show that the suggested method is accurate and can be used to protect power grids from LR attacks.