--This paper introduces a novel approach to the power system security assessment using Multi-T ask Learning (MTL), and reformulating the problem as a multi-label classification task. The proposed MTL framework simultaneously assesses static, voltage, transient, and small-signal stability, improving both accuracy and interpretability with respect to the most state of the art machine learning methods. It consists of a shared encoder and multiple decoders, enabling knowledge transfer between stability tasks. Experiments on the IEEE 68-bus system demonstrate a measurable superior performance of the proposed method compared to the extant state-of-the-art approaches. The power system security assessment (PSSA) is essential power application in energy management systems [1] apparatus that ensures the reliability and stability of energy delivery [2]. Power system operators routinely perform security assessments to ensure the system can withstand disturbances, typically involving steady-state and dynamic simulations every 15 minutes to prepare contingency plans for critical scenarios [3]. In recent years, mainly due to the ongoing changing landscape in the energy mix of electricity grids around the globe, conducting real-time PSSA has become more complex to the point that many power utilities may abandon this critical function. Instead, they rely solely on static security assessment, risking blackout as a result of dynamic instabilities.

This paper introduces a framework to capture previously intractable optimization constraints and transform them to a mixed-integer linear program, through the use of neural networks. We encode the feasible space of optimization problems characterized by both tractable and intractable constraints, e.g. differential equations, to a neural network. Leveraging an exact mixed-integer reformulation of neural networks, we solve mixed-integer linear programs that accurately approximate solutions to the originally intractable non-linear optimization problem. We apply our methods to the AC optimal power flow problem (AC-OPF), where directly including dynamic security constraints renders the AC-OPF intractable. Our proposed approach has the potential to be significantly more scalable than traditional approaches. We demonstrate our approach for power system operation considering N-1 security and small-signal stability, showing how it can efficiently obtain cost-optimal solutions which at the same time satisfy both static and dynamic security constraints.