Digital Twins of hydroelectric generating units: AI insight from sensor data combined with physics-based simulation

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The operation of hydroelectric generating units is evolving, creating the need for better condition monitoring and accurate modeling of the power-generating equipment. Whereas hydro units used to be overdesigned and continuously operated at baseload conditions, today's new units and refurbished ones are designed with stringent performance and operational regime requirements. Notably, they are relied upon for power regulation to integrate more intermittent wind and solar energy in the grid and must undergo frequent starts and stops. The consequence of all this is greater stresses on the units, which can lead to failures and loss of productivity. It is thus essential to better monitor the equipment and predict its behaviour and outcome. Polytechnique Montreal, Hydro Quebec and Maya HTT will work together to develop the tools to build the Digital Twin of a hydro unit. Such a digital twin will combine live sensor data with physics-based modeling through artificial intelligence to achieve real-time simulation of a hydro unit. It will allow predicting failures, optimising maintenance schedules, and simulate scenarios of usage and wear of the equipment. To this end, a group of more than 10 highly qualified personnel will use Physics-Informed Neural Networks and Proper Generalized Decomposition to develop reduced-order models of academic model systems exhibiting some of the same physics as Hydro Quebec's equipment and incorporate them in Maya HTT's software solution. This gradual approach will enable us to overcome the same challenges required to combine experimental measurements and physical modeling with artificial intelligence to develop digital twins for real industrial equipment.