Wang, H.; Ou, S.S.; Dahlhaug, O.G.; Storli, P.-T.; Skjelbred, H.I.; Vilberg, I. Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System. Mathematics2023, 11, 4012.
Wang, H.; Ou, S.S.; Dahlhaug, O.G.; Storli, P.-T.; Skjelbred, H.I.; Vilberg, I. Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System. Mathematics 2023, 11, 4012.
Wang, H.; Ou, S.S.; Dahlhaug, O.G.; Storli, P.-T.; Skjelbred, H.I.; Vilberg, I. Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System. Mathematics2023, 11, 4012.
Wang, H.; Ou, S.S.; Dahlhaug, O.G.; Storli, P.-T.; Skjelbred, H.I.; Vilberg, I. Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System. Mathematics 2023, 11, 4012.
Abstract
In the development of a digital twin (DT) for hydropower turbines, dynamic modeling of the system (e.g., penstock, turbine, speed control) is crucial, along with all the necessary data interface, virtualization, and dashboard designs. Since the DT must mimic the actual dynamics of the hydropower turbine accurately, adaptive learning is required to train these dynamic models online so that the models in the DT can effectively follow the representation of the actual hydropower turbine dynamics accurately and reliably. This study presents an adaptive learning method for obtaining the hydropower turbine models for DT development of hydropower systems using recursive least squares algorithm. To simplify the formulation, the hydropower turbine under consideration was assumed to operate near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters. In this context, the well-known six-coefficient model for the Francis turbine was formulated as the starting point to obtain input and output models for the turbine. Then, an adaptive learning mechanism was developed to learn model parameters using the real-time data from a hydropower turbine testing system. This led to a semi-physical modeling in which the first principles and data-driven modeling are integrated to produce dynamic models for the DT development. Applications to a pilot system at the Norwegian University of Science and Technology (NTNU) were made, and the models learned adaptively using the data collected from the university’s pilot system. Desired modeling and validation results were obtained.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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