电力信息物理系统中基于自适应卡尔曼滤波的动态负载变化攻击检测OA

Addressing the threat of dynamic load-altering attacks(D-LAA)on the load sides in cyber physical power systems(CPPS),in this paper we analyzed the impacts of such attacks on power system operation and proposed a detection method based on the state estimator.First,from the attacker's perspective,a D-LAA was designed using a proportional-integral controller.Simulations verified that the proposed attack model induced greater fluctuations and deviations in the victim load's power and the generator rotor frequencies,while also requiring less time to execute.Then,from the defender's perspective,a state estimator was designed.To overcome the limitations of the standard Kalman filter—specifically its high dependency on accurate noise statistics and increased estimation errors under attack conditions—an improved adaptive fading-factor Kalman filter algorithm was introduced.In this algorithm,the fading factor was derived from the estimated innovation covariance.Finally,an attack detection method was developed based on the state estimation results.Simulation results showed that the proposed algorithm achieved higher estimation accuracy of and greater sensitivity to abnormal signals,and the detection method could identify the attack signals promptly,thereby establishing a defensive barrier for the secure and stable operation of CPPS.