Digital Twin User Guide for Chelan County Public Utility District
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Chelan County Public Utility District, Wenatchee, WA (United States)
- New York Univ. (NYU), NY (United States)
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
This user manual offers a comprehensive guide for developing a Digital twin (DT) of a Kaplan turbine at Chelan County Public Utility District (Chelan PUD) using neural networks. As variable renewable generation expands, hydropower units must operate with optimal efficiency and stability. For Kaplan machines, this flexibility is achieved through coordinated control of guide vane (wicket gates) opening and runner blade pitch, which amplifies the plant’s inherent nonlinear behavior and challenges traditional physics-only modeling. The efficiency of the Kaplan turbine varies with different combinations of the guide vans (wicket gate) opening and the blade angle. Each guide van opening and blade angle has a corresponding highest efficiency point, forming a cam relationship that represents the optimal combination.The discharge of a hydraulic turbine is controlled by the opening angle of the guide vans. Therefore, for each value of head, there is a certain guide van opening and blade angle that corresponds to the highest efficiency. For a given head, different combinations of the guide van opening and blade angle have different efficiencies. Therefore, coordinate cam curves are used to describe the relationship between the wicket gate opening and blade angle with different water head. To address these challenges, the manual details a data-driven modeling and learning workflow centered on structured neural networks. The approach is designed to forecast critical operational variables—discharge flow, net head, penstock (or scroll-case) pressure, and generator electrical outputs—by leveraging real-time inputs such as the generator power control setpoint, exciter field current and field voltage, together with hydromechanical commands (e.g., gate position and, when available, runner blade-pitch angle). The neural models are trained and validated on operational data from a Kaplan unit operated by Chelan PUD, demonstrating that the structured NN architecture can learn the coupled gate–blade–electrical dynamics. The result is a robust DT that improves situational awareness and supports data-informed decision-making for Chelan PUD’s Kaplan turbine operations.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- DOE Contract Number:
- AC05-00OR22725;
- OSTI ID:
- 3018112
- Report Number(s):
- ORNL/TM--2026/4447
- Country of Publication:
- United States
- Language:
- English
Similar Records
Troubleshooting for efficiency improvements and life extension in Kaplan type turbines
Method and apparatus for automatically index testing a Kaplan turbine
Conference
·
Sat Dec 30 23:00:00 EST 1995
·
OSTI ID:220079
Method and apparatus for automatically index testing a Kaplan turbine
Patent
·
Mon Dec 26 23:00:00 EST 1988
·
OSTI ID:6153906