Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System

Hong Wang (), Shiqi (Shawn) Ou, Ole Gunnar Dahlhaug, Pål-Tore Storli, Hans Ivar Skjelbred and Ingrid Vilberg
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Hong Wang: Buildings and Transportation Science Division, Oak Ridge National Laboratory, Knoxville, TN 37932, USA
Shiqi (Shawn) Ou: Buildings and Transportation Science Division, Oak Ridge National Laboratory, Knoxville, TN 37932, USA
Ole Gunnar Dahlhaug: Waterpower Laboratory, Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway
Pål-Tore Storli: Waterpower Laboratory, Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway
Hans Ivar Skjelbred: SINTEF, Torgarden, 7465 Trondheim, Norway
Ingrid Vilberg: SINTEF, Torgarden, 7465 Trondheim, Norway

Mathematics, 2023, vol. 11, issue 18, 1-20

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 the 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 real-time data from a hydropower turbine testing system. This led to semi-physical modeling, in which first principles and data-driven modeling are integrated to produce dynamic models for 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.

Keywords: hydropower systems; Francis turbine; synchronous generator; dynamic modeling; adaptive learning; simulations; 68Q32 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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