Intelligent Anomaly Detection for Large-scale Smart Grids

This paper proposes an unsupervised anomaly detection scheme based on statistical correlation between measurements. The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent cyber-attack, and can be implemented in. Simulation results on IEEE 39, 118 and 2848 bus systems verify the efficiency and accuracy of the proposed method under different operation conditions. The results show accuracy of 99%, true positive rate of 98% and false positive rate of less than 2%.