  The security of energy supply in a power grid critically depends on the
ability to accurately estimate the state of the system. However, manipulated
power flow measurements can potentially hide overloads and bypass the bad data
detection scheme to interfere the validity of estimated states. In this paper,
we use an autoencoder neural network to detect anomalous system states and
investigate the impact of hyperparameters on the detection performance for
false data injection attacks that target power flows. Experimental results on
the IEEE 118 bus system indicate that the proposed mechanism has the ability to
achieve satisfactory learning efficiency and detection accuracy.
