Research on dynamic modeling and simulation of axial-flow pumping system based on RBF neural network

Dynamic model is an important issue for research on stability, dynamic characteristics, surge and control technique of axial-flow pumping system, and such a model is usually characterized by complex nonlinearity, strong coupling and time-varying mathematical equation. For the convenience of establishing model and highly effective computing, dynamic characteristics of the whole system are divided into four parts: pump lift-flow characteristics, pipeline characteristics, mechanical characteristics of asynchronous motor and torque characteristics of pump load. Each part is a nonlinear subsystem, and there are complex coupling relations among each other. In the paper, each part of the pump system is modeled respectively by mechanisms of hydrodynamics, transmission dynamics, electromechanics and affinity law. Considering that the axial-flow pump is characterized by nonlinearity and parameters are difficultly estimated in the low flow operation area and that the data of pump head-flow can be easily tested under the speed of power frequency, a modeling method combined with the RBF neural network is proposed, where hidden layer parameters are optimized by K means clustering algorithm, and the weights are trained by least square method. At last the whole simulation model of the axial-flow pumping system is set up, and the validity of the proposed modeling method is verified through simulation.