  Despite the relentless progress of deep learning models in analyzing the
system conditions under cyber-physical events, their abilities are limited in
the power system domain due to data availability issues, cost of data
acquisition, and lack of interpretation and extrapolation for the data beyond
the training windows. In addition, the integration of distributed energy
resources (DERs) such as wind and solar generations increases the complexities
and nonlinear nature of power systems. Therefore, an interpretable and reliable
methodology is of utmost need to increase the confidence of power system
operators and their situational awareness for making reliable decisions. This
has led to the development of physics-informed neural network (PINN) models as
more interpretable, trustworthy, and robust models where the underlying
principled laws are integrated into the training process of neural network
models to achieve improved performance. This paper proposes a multivariate
physics-informed convolutional autoencoder (PIConvAE) model to detect cyber
anomalies in power distribution systems with unbalanced configurations and high
penetration of DERs. The physical laws are integrated through a customized loss
function that embeds the underlying Kirchhoff's circuit laws into the training
process of the autoencoder. The performance of the multivariate PIConvAE model
is evaluated on two unbalanced power distribution grids, IEEE 123-bus system
and a real-world feeder in Riverside, CA. The results show the exceptional
performance of the proposed method in detecting various cyber anomalies in both
systems. In addition, the model's effectiveness is evaluated in data scarcity
scenarios with different training data ratios. Finally, the model's performance
is compared with existing machine learning models where the PIConvAE model
surpasses other models with considerably higher detection metrics.
