  This paper develops a novel machine learning-based framework using
Semi-Supervised Multi-Task Learning (SS-MTL) for power system dynamic security
assessment that is accurate, reliable, and aware of topological changes. The
learning algorithm underlying the proposed framework integrates conditional
masked encoders and employs multi-task learning for classification-aware
feature representation, which improves the accuracy and scalability to larger
systems. Additionally, this framework incorporates a confidence measure for its
predictions, enhancing its reliability and interpretability. A topological
similarity index has also been incorporated to add topological awareness to the
framework. Various experiments on the IEEE 68-bus system were conducted to
validate the proposed method, employing two distinct database generation
techniques to generate the required data to train the machine learning
algorithm. The results demonstrate that our algorithm outperforms existing
state-of-the-art machine learning based techniques for security assessment in
terms of accuracy and robustness. Finally, our work underscores the value of
employing auto-encoders for security assessment, highlighting improvements in
accuracy, reliability, and robustness. All datasets and codes used have been
made publicly available to ensure reproducibility and transparency.
