本文提出了一种有效可行的电力系统动态负荷变化攻击检测方法。首先，建立了基于D-LAAs的智能电网离散系统模型。其次，设计了一种自适应衰落卡尔曼滤波器（AFKF）来估计智能电网的状态，AFKF能完全滤除电力系统的高斯噪声，得到更准确的状态变化曲线（包括攻击），又提出了一种基于AFKF的欧氏距离比检测算法。通过新的欧氏距离比放大不可感知的D-LAA，提高了D-LAA的检测灵敏度，特别是对于非常弱的D-LAA攻击。最后，通过仿真验证了欧氏距离比检测算法的可行性和有效性。“,”This paper presents an effective and feasible method for detecting dynamic load-altering attacks (D-LAAs) in a smart grid. First, a smart grid discrete system model is established in view of D-LAAs. Second, an adaptive fading Kalman filter (AFKF) is designed for estimating the state of the smart grid. The AFKF can completely filter out the Gaussian noise of the power system, and obtain a more accurate state change curve (including consideration of the attack). A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs. Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity, especially for very weak D-LAA attacks. Finally, the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations. Keywords: Adaptive fading Kalman filter, Dynamic load, Attack detection.