Fine Zoning of Axial Flow Kaplan Turbine Operating Regions Based on Pressure Pulsation Visualization Tracking

Abstract

Under the global context of energy transition and sustainable development, enhancing the operational efficiency of hydraulic turbines and reducing energy losses are significant for the hydropower industry. This study, based on entropy generation theory and pressure fluctuation tracking network methodology, combines numerical simulations with experimental research to thoroughly investigate the energy characteristics and operational features of Kaplan turbines under different guide vane and runner blade matching conditions. Additionally, a Turbine Operating Zone Density Clustering Algorithm (TOZ-DBSCAN) is proposed for operational condition point classification in Kaplan turbines. The following results and conclusions were drawn: The energy loss within the draft tube is the primary cause of efficiency decline in hydraulic turbines. The internal pressure fluctuation intensity of the turbine is highest when the runner blade is at 0°. When the blade opening is at -5°, the overall entropy generation of the unit remains relatively stable, with lower energy losses. The maximum entropy generation in the H3 (High-head 3) operational condition is relatively high. The TOZ-DBSCAN method effectively identifies "inferior" operational condition points characterized by low efficiency and high fluctuations. The findings of this study provide theoretical support for the stable operation and efficient regulation of hydraulic turbines.