A Diffusion-based Generative Machine Learning Paradigm for Contingency Screening

Contingency screening is a crucial part of electric power systems all the time. Power systems frequently encounter multiple challenging operational dilemmas that could lead to the instability of power systems. Contingency analysis is effort-consuming by utilizing traditional numerical analysis methods. It is commonly addressed by generating a whopping number of possible contingencies or manipulating network parameters to determine the worst scenarios. This paper proposes a novel approach that diverts the nature of contingency analysis from pre-defined scenario screening to proactive-unsupervised screening. The potentially risky scenarios of power systems are generated from learning how the previous ones occurred. In other words, the internal perturbation that initiates contingencies is learned prior to being self-replicated for rendering the worst scenarios. By leveraging the perturbation diffusion technique, a proposed model is built to point out the worst scenarios instead of repeatedly simulating one-by-one scenarios to define the highest-risk ones. Empirical experiments are implemented on the IEEE systems to test and validate the proposed solution.