Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for selecting, from a unified electrical power grid model, a first proper subset of elements of the model. A first electrical grid simulation model is selected. A second, differing proper subset of elements of the model are selected. A second electrical grid simulation model that differs from the first electrical grid simulation model is selected. A set of boundary conditions common to the first electrical grid simulation model and the second electrical grid simulation model are determined. Operation of the electrical power grid is simulated, and can include (i) simulating, using the first electrical grid simulation model, the first proper subset of elements of the unified model, and the set of boundary conditions; and (ii) simulating, using the second electrical grid simulation model and the second proper subset of elements of the unified model, and the set of boundary conditions.

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for predicting electrical component failure. A first sensor measurement of a component of an electrical grid taken at a first time can be obtained. A second sensor measurement of the component taken at a second time can be identified, and the second time can be after the first time. An input, which can include the first sensor measurement and the second sensor measurement, can be processed using a machine learning model that is configured to generate, based on one or more changes in one or more characteristics of the component as depicted in the second sensor measurement compared to the first sensor measurement, a prediction representative of a likelihood that the component will experience a type of failure during a time interval. Data indicating the prediction can be provided for presentation by a display.