Abstract:

To address the insufficient reliability and practicality in the optimal flow allocation scheme, which arises from flow monitoring errors during water pumping at diversion pump stations, we developed an optimal flow allocation model based on the particle swarm optimization algorithm. This model reveals how the number of pumping units and overall efficiency respond to flow monitoring errors, and identifies a sensitive range for the optimal number of power-on pumping units to guide real-time control strategies. With the Liyuzhou Pumping Station from the Pearl River Delta Water Resources Allocation Project as a case study, our results indicate that the sensitive range for the optimal number of pumping units primarily concentrates in the transition zone during number adjustments. When the flow monitoring error remains constant, this sensitive range expands as flow increases, with the proportion of the sensitive range positively correlated to the absolute value of the monitoring error. Additionally, flow monitoring error leads to changes (fluctuating within 2%) in the efficiency of pumping station. Consequently, when making start-up plans for the pumping station, it is crucial to pay special attention to flow monitoring errors in the transition interval of operating conditions, as these conditions significantly impact the necessity for unit regulation adjustments.

Key words: water diversion pump station, optimal flow allocation, sensitivity analysis, flow monitoring error, particle swarm optimization algorithm