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This article proposes a remedial action scheme (RAS) based on the concept of deep learning to mitigate the impacts of false data injection (FDI) cyberattacks on smart power systems. As a prerequisite of such a RAS, power system operator is being in attacker's shoe to scrutinize different scenarios of cyberattacks. In design of the RAS, long short-term memory (LSTM) cells have been integrated into a deep recurrent neural network to effectively process the data of an intelligent archive framework (IAF), identifying the proper reaction mechanisms. Power flow analysis has been considered to examine the link between transmission/distribution sectors to react to the cyberattacks for which similar preinvestigated remedial actions have not been saved in the IAF. Effectiveness of the proposed RAS is validated on two IEEE transmission/distribution systems, where consequences of FDI cyberattacks are reduced by 30% in case of experiencing attacks, which are not preinvestigated by system operator.

In this article, we scrutinize a false data injection cyberattack, which can cause market power in favor of a group of aggregators participating in a deregulated electricity market. Therefore, an attacker on behalf of a group of retailers gains access and manipulates the data control unit of other retailers. The main objective of the attacker is to compromise the information associated with the (to be attacked) market participants. Such a cyberattack can lead to a market power since the system operator will need to accept any price offered by the nonattacked retailers to prevent the shortage of supply. This article also proposes a remedial action scheme (RAS) to mitigate the consequences of such a cyberattack by running a backup market. The effectiveness of the proposed RAS is validated on the 136-bus distribution system modified to become unbalanced and contain retailers of photovoltaic, wind turbines, smart homes, and diesel engines.