  Detection of cyber attacks in smart power distribution grids with unbalanced
configurations poses challenges due to the inherent nonlinear nature of these
uncertain and stochastic systems. It originates from the intermittent
characteristics of the distributed energy resources (DERs) generation and load
variations. Moreover, the unknown behavior of cyber attacks, especially false
data injection attacks (FDIAs) in the distribution grids with complex temporal
correlations and the limited amount of labeled data increases the vulnerability
of the grids and imposes a high risk in the secure and reliable operation of
the grids. To address these challenges, this paper proposes an unsupervised
adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power
distribution grids integrated with DERs, i.e., PV systems and wind generation.
The proposed method utilizes long short-term memory (LSTM) in the structure of
the autoencoder to capture the temporal dependencies in the time-series
measurements and leverages the power of generative adversarial networks (GANs)
for better reconstruction of the input data. The advantage of the proposed
data-driven model is that it can detect anomalous points for the system
operation without reliance on abstract models or mathematical representations.
To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and
123-bus systems with historical meteorological data (wind speed, ambient
temperature, and solar irradiance) as well as historical real-world load data
under three types of data falsification functions. The comparison of the
detection results of the proposed model with other unsupervised learning
methods verifies its superior performance in detecting cyber attacks in
unbalanced power distribution grids.
