Abstract
Modern power systems are constantly subjected to various disturbances, device failures, as well as data attacks. To improve the quality of monitoring and control in smart grid, researchers have conducted extensive studies in exploring the advantages of real-time digital meters such as the Phasor Measurement Units, combining with dynamic estimation methods such as Kalman filters. Standard Kalman filter assumes we have statistical knowledge regarding the uncertainty of the system under study. The reality is, the accurate system model is almost impossible to obtain, especially with the existence of malicious data attack. A lightweight and efficient adaptive Kalman filter algorithm is presented in this paper for its ability to alleviate the impact of incorrect system models and/or measurement data. Simulations demonstrate that it is resilient to suboptimal system modeling, sudden system dynamic changes and bad data injection.
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