Correction of Pump Characteristic Curves Integrating Representative Operating Condition Recognition and Affine Transformation

To address the need for intelligent scheduling and model integration under spatiotemporal variability and uncertainty in water systems, this study proposes a hybrid correction method for pump characteristic curves that integrates data-driven techniques with an affine modeling framework. Steady-state data are extracted through adaptive filtering and statistical testing, and representative operating conditions are identified via unsupervised clustering. An affine transformation is then applied to the factory-provided characteristic equation, followed by parameter optimization using the clustered dataset. Using the Hongze Pump Station along the eastern route of the South-to-North Water Diversion Project as a case study, the method reduced the mean blade angle prediction error from 1.73° to 0.51°, and the efficiency prediction error from 7.32% to 1.30%. The results demonstrate improved model accuracy under real-world conditions and highlight the method’s potential to support more robust and adaptive hydrodynamic scheduling models, contributing to the advancement of sustainable and smart water resource management.