Technical Report


 

Model Identification and Validation of Switched Reluctance Machines from Test Data
 
Wenzhe Lu, Ph.D Student
Ali Keyhani, Professor of Electrical Engineering
 
The Ohio State University
Electrical Engineering Department
Columbus Ohio 43210
Tel: 614-292-4430
Fax: 614-292-7596
Keyhani.1@osu.edu
June 11, 2000
 

 ABSTRACT: Switched reluctance machines (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 SRMs 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 the SRM is often needed. At the same time, to increase reliability and reduce cost, sensorless (without rotor position/speed sensor) controllers are preferred. With the rapid progress in microprocessors (DSP), MIPS-intensive control techniques such as sliding mode observers and controllers 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 changes 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 and the inductance model. 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. Both models got wide applications. In our research, the inductance model will be used.

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 maximum likelihood estimation (MLE) techniques. This method has already been applied successfully to identify the model and parameters of induction and synchronous machines. Furthermore, loading will affect the operating temperature of the machine due to core losses. Thus, for accurate modeling, operating data under load needs to be used for final model development.

In this report, the procedures to identify an 8/6 SRM parameters from standstill test data and the test results are presented after a brief introduction to the inductance model of SRM. Model validation through on-line test is also given. Further improvement and validation of the model and parameters will be performed.

 
 

If your company is a member of the Mechatronic Laboratory, please send the request to receive a copy of any technical report. If you are not a member please send a request to Ali Keyhani, Department of Electrical Engineering, Mechatronics Program at the following address: Ali Keyhani, Ohio State University, Electrical Engineering Department, Mechatronics Systems Laboratory, 2015 Neil Ave., 205 Dereese Lab., Columbus, OH 43210.

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