The invention discloses a high-precision modeling method for an axial-flow
Kaplan turbine based on a seagull optimization
algorithm and a BP (Back Propagation) neural network. The high-precision modeling method comprises the following steps: acquiring basic modeling data of the axial-flow
Kaplan turbine; the opening degree characteristic and efficiency characteristic data of the axial-flow
Kaplan turbine are supplemented; constructing a flow characteristic and torque characteristic
data set of the axial-flow Kaplan
turbine; correcting the guide vane opening degree in the flow characteristic and torque characteristic
data set of the axial flow Kaplan
turbine; constructing a guide vane and
paddle joint characteristic model based on a seagull optimization
algorithm and a BP neural network; and in combination with the corrected
data set, the BP neural network and a seagull optimization
algorithm, reconstructing a flow characteristic and torque characteristic neural
network model of the axial-flow Kaplan
turbine. According to the axial-flow movable
propeller water turbine modeling method considering the modeling
data quality and the guide vane and
paddle joint characteristics, a high-precision and nonlinear axial-flow movable
propeller water turbine model can be obtained, and a model basis is provided for axial-flow movable
propeller water turbine unit control
strategy research.