Abstract
The application of electrical drives in the control of centrifugal fans and pumps is a well-established area that leads to substantial energy savings. It requires electrical and automation engineers to have some knowledge relevant to drives about fan and pump modelling since ignoring or oversimplifying fan/pump modelling for the intended drive design compromises the required control quality. This paper improves the existing dynamical models of fans and pumps integrated with induction motors via neural network estimation of overall fan/pump efficiency; this estimation is based on the voltage frequency of the driving induction motor, pressure, and flow rate, followed by the separation of the fan’s or pump’s own efficiency for the motor load torque computation, enhancing the accuracy due to the correct reflection of the power balance. The model is developed with a view to being convenient for control design applications. For the first time for dynamical models, it is verified experimentally, justifying the necessity of the first-order nonlinear differential equation for the flow rate. The validation includes a direct approach based on analysis of transient behaviour caused by a small-step perturbation of the frequency of the motor voltages and an indirect approach based on the introduced concept of the dynamical flow rate and pressure estimators.
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