Revisiting security threat on smart grids: Accurate and interpretable fault location prediction and type classification

This work revisits security threats on smart electrical grids and focuses on the dimensions of dependability, serviceability, and accountability, which constitute the security requirements of an SG application. The first two dimensions deal with fault diagnosis and location, while the last element tackles building the system more transparent. We proposed a data-driven machine-learned fault prediction system that can provide abrupt and accurate fault type classification and location prediction. Furthermore, we reported a feature interaction visualization and elaborated on how this step can facilitate interpretation of the results and assessment of the security threats in the SG. The evaluation of the system is carried out on a large-scale dataset comprised of approximately 1.9K training samples. Results show the effectiveness of the proposed system both in prediction and interpretability steps.1