Additional Mechanical Torque Coefficients of Hydro Turbine and Governor System

Zeng, Yun; Zhang, Li Xiang; Qian, Jing; Guo, Ya Kun; Xu, Tian Mao

doi:10.4028/www.scientific.net/amr.443-444.954

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…The operating conditions of the HTGS change with changes in D. It reflects the inherent damping level of the generator and has a significant influence on the stability of the generator linked with the power grid (for details see [9]). Hence, it is interesting to investigate the dynamic behaviour of the HTGS under the variation of D.…”

Section: Dynamic Analysis Of the Htgs As D Changesmentioning

confidence: 99%

See 1 more Smart Citation

Local bifurcation and continuation of a non‐linear hydro‐turbine governing system in a single‐machine infinite‐bus power system

Zhang

Chen

et al. 2020

IET Generation, Transmission & Distribution

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Section: Dynamic Analysis Of the Htgs As D Changesmentioning

confidence: 99%

“…The damping coefficient is a composite parameter, which includes the mechanical damping coefficient, the electrical damping etc. [9]. Zhang et al [10] applied a novel online monitoring damping coefficient to the Yimin power plant and demonstrated its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Local bifurcation and continuation of a non‐linear hydro‐turbine governing system in a single‐machine infinite‐bus power system

Zhang

Chen

et al. 2020

IET Generation, Transmission & Distribution

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“…We emphasize the establishment of adaptive modeling of water flow and turbine speed control systems. To simplify the formulation, we assumed that the system under consideration operates near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters [20]. In this context, the well-known six-coefficient model [21] for the Francis turbine was formulated as a starting point in the state space form.…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Ou,

Dahlhaug

et al. 2023

Mathematics

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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.

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

Wang,

Ou,

Dahlhaug

et al. 2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Additional Mechanical Torque Coefficients of Hydro Turbine and Governor System

Cited by 3 publications

References 7 publications

Local bifurcation and continuation of a non‐linear hydro‐turbine governing system in a single‐machine infinite‐bus power system

Local bifurcation and continuation of a non‐linear hydro‐turbine governing system in a single‐machine infinite‐bus power system

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

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

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