Optimal defense against security threat on stability of grid-integrated synchronous generator during FDI attack

Recent internet of energy (IoE)-based transactive energy frameworks (TrEFs) have introduced a wide array of information and communication technology (ICT) devices into conventional power systems, significantly increasing their vulnerability to cyber-attacks. The emergence of prosumers – entities that both consume and generate energy at the distribution level – has further intensified this risk. While considerable research has focused on mitigating attacks on the distribution side of the network, the internal dynamics of power-generating units under such threats have received comparatively less attention. This work addresses this gap by proposing an optimal transient control strategy based on a linear-quadratic-Gaussian (LQG) controller to enhance the resilience of power-generating units during cyber-attacks within TrEFs. The control approach is implemented and tested on a single-machine infinite-bus structure. Designed controller’s performance is justified by comparing with other typical and advanced controllers. The robustness of the proposed scheme is evaluated under worst-case conditions, as well as in the other cases, including the simultaneous occurrence of severe system faults and various false data injection (FDI) attacks. Simulation results demonstrate that the proposed controller can regain its marginal operating conditions within 0.93, 0.53 and 0.47 s, when the system is considered under scale, pulse and ramp FDI attack respectively highlighting its effectiveness and reliability. • Examining grid-integrated synchronous generator’s (GiSG) vulnerability in both normal and cyber-attack scenarios. • Developing a robust LQG control act ensuring stability without power system stabilizer (PSS). • Evaluating the designed LQG control’s robustness in diverse operating conditions. • Assessing the designed controller’s performance in worse case condition considering FDI attack along with severe system fault.