LAUSR

Recent developments in power systems to build a smart grid depend critically on communication networks which provide unprecedented advantages for comprehensive system visibility, monitoring, protection, and control. However, it increases the system complexity and vulnerability to cyberattacks that could jeopardize the seamless system operation. The attack must be detected effectively, and then proper measures to mitigate the effect quickly to restore normality. This work implements a reduced-order $H_{\infty }$ filter to simultaneously estimate the state variable and unknown inputs such as noise or attack signals in the automatic generation control (AGC) system. Further, the knowledge of the disturbances is utilized to mitigate the attack by appropriately compensating the sensor readings, such that the control decisions are based on the correct states, not the corrupted signals generated in the attack scenario. The stability criterion is provided based on Lyapunov functions to check the estimation error in the presence of the exogenous disturbance. Further, the proposed $H_{\infty }$ estimator performance is evaluated by comparing it with the Kalman filter to verify its effectiveness. The estimator’s performance is validated by developing a cosimulation platform using the server–client architecture of data exchange. Further, the performance is validated in a real-time digital simulator (RTDS) platform used as the power system interacting with an estimator deployed on Raspberry Pi board sensing the attacks.