Successful detection of false data injection attacks (FDIAs) is essential for ensuring secure power grids operation and control. First, this paper extends the approximate dc model to a more general linear model that can handle both supervisory control and data acquisition and phasor measurement unit measurements. Then, a general FDIA based on this model is derived and the error tolerance of such attacks is discussed. To detect such attacks, a method based on short-term state forecasting considering temporal correlation is proposed. Furthermore, a statistics-based measurement consistency test method is presented to check the consistency between the forecasted measurements and the received measurements. This measurement consistency test is further integrated with ∞-norm and L ₂ -norm-based measurement residual analysis to construct the proposed detection metric. The proposed detector addresses the shortcoming of previous detectors in terms of handling critical measurements. Besides, the removal problem of attacked measurements, which may cause the system to become unobservable, is addressed effectively by the proposed method through forecasted measurements. Numerical tests on IEEE 14-bus and 118-bus test systems verify the effectiveness and performance of the proposed method.

Advanced distribution management system (DMS), an evolution of supervisory control and data acquisition obtained by extending its working principles from transmission to distribution, is the brain of a smart grid. Advanced DMS assesses smart functions in the distribution system and is also responsible for assessing control functions such as reactive dispatch, voltage regulation, contingency analysis, capability maximization, or line switching. Optimal power flow (OPF)-based tools can be suitably adapted to the requirements of smart distribution network and be employed in an advanced DMS framework. In this paper, the authors present a methodology for unbalanced three-phase OPF (TOPF) for DMS in a smart grid. In the formulation of the TOPF, control variables of the optimization problem are actual active load demand and reactive power outputs of microgenerators. The TOPF is based on a quasi-Newton method and makes use of an open-source three-phase unbalanced distribution load flow. Test results are presented on the IEEE 123-bus Radial Distribution Feeder test case.