  State estimation is of considerable significance for the power system
operation and control. However, well-designed false data injection attacks can
utilize blind spots in conventional residual-based bad data detection methods
to manipulate measurements in a coordinated manner and thus affect the secure
operation and economic dispatch of grids. In this paper, we propose a detection
approach based on an autoencoder neural network. By training the network on the
dependencies intrinsic in 'normal' operation data, it effectively overcomes the
challenge of unbalanced training data that is inherent in power system attack
detection. To evaluate the detection performance of the proposed mechanism, we
conduct a series of experiments on the IEEE 118-bus power system. The
experiments demonstrate that the proposed autoencoder detector displays robust
detection performance under a variety of attack scenarios.
