ABSTRACT:
Switched reluctance motors (SRM) have undergone rapid development in hybrid electric vehicles, aircraft starter/generator systems, washing machines, and automotive applications over the last two decades. This is mainly due to the various advantages of SRM’s over other electric motors such as simple and robust construction, and fault-tolerant performance.
In most of these applications, speed and torque control are necessary. To obtain high quality control, an accurate model of SRM is often needed. At the same time, to increase reliability and reduce cost, sensorless controllers (without rotor position/speed sensor) are preferred. With the rapid progress in microprocessors (DSP), MIPS-intensive control techniques such as sliding mode observers and controllers [1] become more and more promising. An accurate nonlinear model of the SRM is essential to realize such control algorithms.
The nonlinear nature of SRM and high saturation of phase winding during operation makes the modeling of SRM a challenging work. The flux linkage and phase inductance of SRM change with both the rotor position and the phase current. Therefore the nonlinear model of SRM must be identified as a function of the phase current and rotor position. Two main models of SRM have been suggested in the literature – the flux model [2] and the inductance model [3]. In the latter one, “the position dependency of the phase inductance is represented by a limited number of Fourier series terms and the nonlinear variation of the inductance with current is expressed by means of polynomial functions” [3]. This model can describe the nonlinearity of SRM inductance quite well.
Once a model is selected, how to identify the parameters in the model becomes an important issue. Finite element analysis can provide a model that will be subjected to substantial variation after the machine is constructed with manufacturing tolerances. Therefore, the model and parameters need to be identified from test data. As a first step, the machine model can be estimated from standstill test using output error estimation (OE) or maximum likelihood estimation (MLE) techniques. This method has already been applied successfully to identify the model and parameters of induction and synchronous machines [4, 5].
Furthermore, during online operation, the model structures and parameters of SRM’s may differ from the standstill ones because of saturation and losses, especially at high current. To model this effect, a damper winding may be added into the model structure, which is in parallel with the magnetizing winding. The magnetizing current and damper current are highly nonlinear functions of phase voltage, rotor position, and rotor speed. They are not measurable during operation, and are hard to be expressed with analytical functions. Neural network mapping are usually good choices for such tasks [7, 8, 9]. A 2-layer recurrent neural network has been adopted here to estimate these two currents, which takes the phase voltage, phase current, rotor position and rotor speed as inputs. When the damper current is estimated and damper voltage is computed, the damper parameters can be identified using output error or maximum likelihood estimation techniques.
In this report, the procedures to identify an 8/6 SRM parameters from standstill test data are presented after an introduction to the inductance model of SRM. Then a 2-layer recurrent neural network is constructed, trained and applied to identify the damper parameters of SRM from operating data. Model validation through on-line test is also given, which proves the applicability of the proposed methods.
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