Speaker
Description
Large blackouts are important because they have a high impact on our society, and, although rare, are not rare enough to be low risk. Indeed, the heavy tails in their statistics make them high risk. The observed data for large blackouts is limited, which poses challenges to analysis and prevents the training of AI models. However, large blackouts combine different processes at multiple time and spatial scales, and we can learn this anatomy and extract from observed utility data statistical descriptions of the initiation and propagation of outages in the grid. This leads to sampling from generative models that can produce typical patterns of outages. We illustrate such a generative model for the fast propagation of protection system outages caused by protection system misoperations. The generative model can generate synthetic samples with statistical properties similar to the original data. One objective for the new generative models is to leverage the limited observed outage data in order to generate synthetic data samples that can be used in training AI models that account for extreme events in the power grid.
