Detection and countermeasure integrated framework against false data injection attacks in multi-area coupled power systems.

False data injection attacks (FDIA) pose severe threat to the secure operation of load frequency control (LFC) systems. The accuracy of state estimation in power systems directly determines the detection precision and countermeasure effectiveness against FDIA. However, the existing state estimation algorithms are constrained by linear assumptions and the static noise models, making them difficult to adapt to the multi-area coupling characteristics of LFC systems. Moreover, the statistical overlap between the state variables and the noise cannot be effectively handled, further limiting estimation accuracy. Thus, an integrated framework for joint detection and countermeasure against FDIA in multi-area coupled power systems is proposed. In the framework, the sigma-point sampling strategy of the unscented kalman filter is employed to efficiently capture the dynamic characteristics of multi-area coupled LFC systems, thereby improving the adaptability of state estimation algorithm. In addition, an adaptive covariance update mechanism is utilized to track the statistical characteristics of noise and states in real time, reducing the impact of their overlap. Experimental results demonstrate that the proposed framework achieves more accurate state estimation compared with various state estimation algorithms. Moreover, under multiple attack scenarios, it provides superior detection accuracy and more effective countermeasures against FDIA.