Kalman Filters for Dynamic and Secure Smart Grid State Estimation

Combining dynamic state estimation methods such as Kalman filters with real-time data generated/collected by digital meters such as phasor measurement units (PMU) can lead to advanced techniques for improving the quality of monitoring and controllability in smart grids. Classic Kalman filters achieve optimal performance with ideal system models, which are usually hard to obtain in practice with unexpected disturbances, device failures, and malicious data attacks. In this work, we introduce and compare a novel method, viz. adaptive Kalman Filter with inflatable noise variances, against a variety of classic Kalman filters. Extensive simulation studies demonstrate the powerful ability of our proposed algorithm under suboptimal conditions such as wrong system modeling, sudden disturbance and bad data injection.