With the wide application of communication technology in the smart grid, the threat of information attack to the safe operation of the grid also follows. Attacks based on state estimation using phasor measurement techniques in power systems are difficult to detect successfully. Aiming at this problem, this paper proposes an intelligent attack detection method based on phasor measurement data state estimation for power systems. In this method, the self-encoder is used to extract the features of the power grid measurement data, and the feature dimension is gradually reduced by extracting the features multiple times. The final extracted information is subjected to supervised learning through the softmax layer, resulting in an attack detection algorithm based on stacked autoencoders. Aiming at the overfitting problem of autoencoders, an attack detection method based on noise reduction autoencoders is further proposed. The proposed method is simulated and verified by IEEE-118 node test system. The results show that the proposed attack detection method has higher computational accuracy and efficiency than other methods.