This work presents an innovative hybrid methodology for the dynamic modeling of hydraulic turboalternator Groups (HTG). The proposed approach integrates multi-domain physical models with a Hybrid Stochastic Automaton (HSA) implemented through the PyCATSHOO framework, combined with a Long Short-Term Memory (LSTM) neural network for the prediction and adaptive regulation of critical operating states. This intelligent Digital Twin (DT) enables accurate simulation of transient behavior in HTG, particularly under rapid load variations, and provides a real-time predictive capability for detecting instability before it propagates through the system. The methodology begins with a complete multi-physics representation of the turbine-shaft-alternator assembly, where hydraulic, mechanical, and electrical subsystems are coupled through nonlinear differential equations. These continuous dynamics are complemented by discrete-event modeling within the HSA, allowing the system to switch between normal and abnormal operating modes based on threshold conditions or LSTM-derived predictions. The LSTM component is trained on historical SCADA data to recognize early signatures of frequency deviation and to support adaptive gain tuning of the PID controller. The results demonstrate a substantial improvement in frequency stability, with reduced oscillations and a significantly shorter stabilization time compared to conventional PID regulation. Additionally, the hybrid approach enhances robustness to abrupt load disturbances and provides a foundation for predictive maintenance strategies. This research highlights the potential of DT-based intelligent control as a key enabler for the modern operation of hydroelectric infrastructures.
References
[1]
Kundur, P. (1994) Power System Stability and Control. McGraw-Hill.
[2]
Machowski, J., Bialek, J.W. and Bumby, J.R. (2008) Power System Dynamics: Stability and Control. 2nd Edition, Wiley.
[3]
Fang, J., Tang, Y., Li, H. and Li, X. (2019) Grid Frequency Regulation by Hydroelectric Power Plants with Renewable Energy Integration. IEEE Transactions on Sustainable Energy, 10, 1978-1987.
[4]
Hou, J., Liu, Q. and Chen, Y. (2023) Adaptive Control Strategies for Hydroelectric Generation Units under Renewable Fluctuations. Energy, 274, Article ID: 127358.
[5]
Tao, F., Zhang, H., Liu, A. and Nee, A.Y.C. (2019) Digital Twin in Industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15, 2405-2415. https://doi.org/10.1109/tii.2018.2873186
[6]
Rasheed, A., San, O. and Kvamsdal, T. (2020) Digital Twin: Values, Challenges and Enablers from a Modeling Perspective. IEEE Access, 8, 21980-22012. https://doi.org/10.1109/access.2020.2970143
[7]
Mitra, S., Das, A. and Goswami, S.K. (2023) Hybrid Modeling of Renewable Integrated Power Systems for Stability Assessment. Energy Systems, 14, 489-508.
[8]
Milano, F., Dörfler, F., Hug, G., Hill, D.J. and Verbič, G. (2019) Foundations and Challenges of Low-Inertia Systems. Proceedings of the IEEE, 107, 2199-2214.
[9]
He, X., Ai, Q., Qiu, R.C. and Zhang, D. (2019) Preliminary Exploration on Digital Twin for Power Systems: Challenges, Framework, and Applications. arXiv:1909.06977.
[10]
Wang, X., Zhao, T., Liu, H. and He, R. (2019) Power Consumption Predicting and Anomaly Detection Based on Long Short-Term Memory Neural Network. Proceedings of the IEEE 4th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), Chengdu, 12-15 April 2019, 487-491. https://doi.org/10.1109/ICCCBDA.2019.8725704
[11]
Sebastián, R. (2022) Hydropower Flexibility for Modern Electrical Grids with Renewable Energy Integration. Energy Conversion and Management, 259, Article ID: 116620.
[12]
Brownlee, J. (2017) Deep Learning for Time Series Forecasting. Machine Learning Mastery.
[13]
Tao, F., Zhang, M., Liu, Y. and Nee, A.Y.C. (2018) Digital Twin Driven Prognostics and Health Management for Complex Equipment. CIRP Annals, 67, 169-172. https://doi.org/10.1016/j.cirp.2018.04.055
[14]
Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y. and Zhang, Y. (2019) Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE Transactions on Smart Grid, 10, 841-851. https://doi.org/10.1109/tsg.2017.2753802
[15]
Hochreiter, S. and Schmidhuber, J. (1997) Long Short-Term Memory. Neural Computation, 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
[16]
Greff, K., Srivastava, R.K., Koutnik, J., Steunebrink, B.R. and Schmidhuber, J. (2017) LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28, 2222-2232. https://doi.org/10.1109/tnnls.2016.2582924
[17]
Schultz, M.G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L.H., et al. (2021) Can Deep Learning Beat Numerical Weather Prediction? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379, Article ID: 20200097. https://doi.org/10.1098/rsta.2020.0097
[18]
Davis, M.H.A. (1984) Piecewise-Deterministic Markov Processes: A General Class of Non-Diffusion Stochastic Models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 46, 353-376. https://doi.org/10.1111/j.2517-6161.1984.tb01308.x
[19]
Siami-Namini, S., Tavakoli, N. and Siami Namin, A. (2018) A Comparison of ARIMA and LSTM in Forecasting Time Series. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, 17-20 December 2018, 1394-1401. https://doi.org/10.1109/icmla.2018.00227
