Cyber attack protection and control in microgrids using channel code and semidefinite programming

Abstract

The smart grid has been considered as a nextgeneration power system to modernize the traditional grid to improve its security, connectivity and sustainability. Unfortunately, the grid is susceptible to malicious cyber attacks, which can create serious technical, economical and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this paper proposes a recursive systematic convolutional (RSC) code and Kalman filter based method in the context of microgrids. Specifically, the proposed RSC code is used to add redundancy in the microgrid states, and the log maximum a posterior is used to recover the state information which is affected by random noises and cyber attacks. Once the estimated states are obtained, a semidefinite programming based optimal feedback controller is proposed to regulate the system states. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate and control the system states.