- By:
- Gui, Yonghao ; Subedi, Sunil ; Wang, Hong ; Yin, Zhun; Jia, Wenbo; Jiang, Zhongping
- Page Number:
- 411-416
- Book Title:
- 2026 IEEE 20th International Conference on Control and Automation (ICCA)
- Publication Date:
- August 12, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- 20th IEEE International Conference on Control and Automation (ICCA)
- Conference Location:
- Almaty, Kazakhstan
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/ICCA69928.2026.11618035
Abstract
This paper presents a comprehensive digital twin (DT) framework for hydroelectric systems that enables adaptive control of turbine governors and excitation systems without requiring detailed manufacturer specifications. The proposed framework integrates neural network-based system identification with stabilizing adaptive control laws for the installed turbine controller and middle-branch adaptive tuning for the installed voltage regulator. Using real operational data from Unit C-8 at Rocky Reach Dam (1,349 MW capacity), highfidelity neural network models are developed to capture turbine and generator dynamics without requiring detailed manufacturer specifications. The DT enables safe controller synthesis and validation in simulation before deployment. For turbine control, the proposed method achieves a 79.9% mean square error (MSE) reduction compared with that of an optimal controller. For voltage regulation, the adaptive excitation controller achieves approximately 42.6% MSE reduction while preserving installed protection logic. The results demonstrate that DT technology provides a practical pathway for modernizing hydropower control systems with minimal operational disruption.
