  This paper presents a hybrid data-driven physics model-based framework for
real time monitoring in smart grids. As the power grid transitions to the use
of smart grid technology, it's real time monitoring becomes more vulnerable to
cyber attacks like false data injections (FDI). Although smart grids
cyber-physical security has an extensive scope, this paper focuses on FDI
attacks, which are modeled as bad data. State of the art strategies for FDI
detection in real time monitoring rely on physics model-based weighted least
squares state estimation solution and statistical tests. This strategy is
inherently vulnerable by the linear approximation and the companion statistical
modeling error, which means it can be exploited by a coordinated FDI attack. In
order to enhance the robustness of FDI detection, this paper presents a
framework which explores the use of data-driven anomaly detection methods in
conjunction with physics model-based bad data detection via data fusion.
Multiple anomaly detection methods working at both the system level and
distributed local detection level are fused. The fusion takes into
consideration the confidence of the various anomaly detection methods to
provide the best overall detection results. Validation considers tests on the
IEEE 118 bus system.
