Emergency Operation of Water Diversion and Power Generation Hubs Based on Adaptive Regulation
Year: 2025
Type: article
Abstract: [Objective] The integrated operation of water diversion and power generation hubs faces challenges in maintaining hydraulic stability during emergencies, particularly under sudden load rejection conditions. This study aims to develop an adaptive emergency regulation strategy to coordinate the operation between diversion sluices and power generation units, with the objectives of minimizing flow and water level fluctuations in the main canal, reducing decision-making response time, and ensuring the operational safety and efficiency of the water conveyance system. This study addresses the critical gap in existing systems where the lack of an adaptive flow regulation mechanism between sluices and turbines leads to prolonged hydraulic transients and potential safety hazards during emergencies. [Methods] A comprehensive methodology combining theoretical analysis, numerical modeling, and prototype validation was adopted. The emergency operation principle was established based on the goal of rapidly restoring flow into the main canal. An adaptive emergency regulation strategy was then developed by integrating the hydraulic relationship between power units and diversion sluices, along with operational constraints such as unit vibration range and gate opening limitations. A one-dimensional hydrodynamic model was established for the canal system extending from the hub to the downstream check gate, simulating unsteady flow under different disturbance scenarios. The model was calibrated and validated using operational data from the prototype. A typical case involving full load rejection from both power generation units was simulated to compare the proposed adaptive strategy with conventional manual operation. Key performance indicators included water level deviation, flow variation, and response time. [Results] The simulation results demonstrated a significant improvement in hydraulic stability under the proposed adaptive emergency strategy. Compared to conventional manual operation, the adaptive approach reduced the maximum water level fluctuation in typical canal cross-sections by over 40%. The maximum flow fluctuation was reduced by 58%, indicating a markedly smoother transition during emergency load rejection. Furthermore, the adaptive strategy enabled an immediate response to emergency conditions, reducing decision-making and execution time to near real-time. The coordination mechanism between the sluice and power generation units effectively balanced flow discontinuities caused by sudden turbine shutdown, thereby limiting the propagation of disturbances along the canal. The hydraulic response under adaptive control showed faster convergence to stable conditions, significantly reducing the risks of overtopping or drying in the canal. [Conclusions] This study successfully develops and validates an adaptive emergency regulation strategy for water diversion and power generation hubs, significantly enhancing operational safety and efficiency during unexpected events. The proposed strategy introduces an innovative real-time coordination mechanism between sluice gates and power generation units, representing a substantial improvement over traditional human-operated systems that often result in delayed and suboptimal responses. The findings provide a scientific and technical foundation for automated emergency management in multi-objective hydraulic hubs. The adaptive method not only ensures rapid flow recovery and minimizes hydraulic transients but also supports the maximization of power generation benefits without compromising water supply security. This approach has broad applicability in similar large-scale water diversion projects worldwide, highlighting its significance in advancing smart water management and emergency response technologies.
Author: NIE Yan-hua, HU Han, HOU Dong-mei
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7092062386
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