Adversarial attack and training for deep neural network based power quality disturbance classification

Abstract

Power quality disturbance (PQD) can significantly affect the normal operation of the power system. Deep neural network (DNN) can classify PQD with extremely high accuracy. However, adversarial attacks can drastically degrade the performance of DNN, which raises security issues for DNN-based PQD classification. At present, there are few researches on adversarial attacks and their defenses in PQD classification. In this study, we first adopt convolutional neural network-long short-term memory (CNN-LSTM) to classify PQD signals. Then we propose an adversarial attack algorithm for PQD classification, i.e., momentum iterative-fast gradient sign method (MI-FGSM). MI-FGSM generates adversarial perturbations along the gradient direction of the loss function by incorporating momentum during iterations. Finally, we propose a defense algorithm to defend against adversarial attacks, i.e., iterative adversarial training (IAT). IAT can improve the robustness of the classification model by making it learn to minimize the worst loss caused by multi-class adversarial perturbation strengths. The experimental results demonstrate that compared with the fast gradient sign method, the MI-FGSM produces smaller perturbations and can further reduce the classification accuracy of CNN-LSTM. In addition, compared with the most advanced adversarial training defense algorithm in PQD classification, IAT can effectively enhance the robustness of the CNN-LSTM against adversarial attacks.