This paper provides a novel bad data detection processor to identify false data injection attacks (FDIAs) on the power system state estimation. The attackers are able to alter the result of the state estimation virtually intending to change the result of the state estimation without being detected by the bad data processors. However, using a specific configuration of an artificial neural network (ANN), named nonlinear autoregressive exogenous (NARX), can help to identify the injected bad data in state estimation. Considering the high correlation between power system measurements as well as state variables, the proposed neural network-based approach is feasible to detect any potential FDIAs. Two different strategies of FDIAs have been simulated in power system state estimation using IEEE standard 14-bus test system for evaluating the performance of the proposed method. The results indicate that the proposed bad data detection processor is able to detect the false injected data launched into the system accurately.
The interdependencies of power systems and natural gas networks have increased due to the additional installations of more environmental-friendly and fast-ramping natural gas power plants. The natural gas transmission network constraints and the use of natural gas for other types of loads can affect the delivery of natural gas to generation units. These interdependencies will affect the power system security and economics in day-ahead and real-time operations. Hence, it is imperative to analyze the impact of natural gas network constraints on the security-constrained unit commitment (SCUC) problem. In particular, it is important to include natural gas and electricity network transients in the integrated system security because the impacts of any disturbances propagate at two distinctly different speeds in natural gas and electricity networks. Thus, analyzing the transient behavior of the natural gas network on the security of natural gas power plants would be essential as these plants are considered to be very flexible in electricity networks. This paper presents a method for solving the SCUC problem considering the transient behavior of the natural gas transmission network. The applicability of the presented method and the accuracy of the proposed solution are demonstrated for the IEEE 118-bus power system, which is linked with the natural gas transmission system and the results are discussed in the paper.
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