TITLE:
Towards an Advanced Digital Twin for the Dynamics of a Hydraulic Turboalternator Group under Variable Load Constraints
AUTHORS:
Desire Tere Djacba, Ruben Torres Martinez, Colince Welba
KEYWORDS:
Digital Twin, Hydraulic Turboalternator Group, LSTM, Hybrid Stochastic Automaton, PyCATSHOO
JOURNAL NAME:
Journal of Computer and Communications,
Vol.14 No.4,
April
21,
2026
ABSTRACT: 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.