Physics-Informed Convolutional Autoencoder for Cyber Anomaly Detection
  in Power Distribution Grids
Mehdi Jabbari Zideh Sarika Khushalani Solanki
The growing trend toward the modernization of power distribution systems has
facilitated the installation of advanced measurement units and promotion of the
cyber communication systems. However, these infrastructures are still prone to
stealth cyber attacks. The existing data-driven anomaly detection methods
suffer from a lack of knowledge about the system's physics, lack of
interpretability, and scalability issues hindering their practical applications
in real-world scenarios. To address these concerns, physics-informed neural
networks (PINNs) were introduced. This paper proposes a multivariate
physics-informed convolutional autoencoder (PIConvAE) to detect stealthy
cyber-attacks in power distribution grids. The proposed model integrates the
physical principles into the loss function of the neural network by applying
Kirchhoff's law. Simulations are performed on the modified IEEE 13-bus and
123-bus systems using OpenDSS software to validate the efficacy of the proposed
model for stealth attacks. The numerical results prove the superior performance
of the proposed PIConvAE in three aspects: a) it provides more accurate results
compared to the data-driven ConvAE model, b) it requires less training time to
converge c) the model excels in effectively detecting a wide range of attack
magnitudes making it powerful in detecting stealth attacks.