Abstract
The switched reluctance machine has been an attractive candidate for many applications owing to its simple design and low construction costs, without the use of permanent magnets. However, the double saliency of its stator and rotor poles results in noise-causing torque ripples. And although advanced torque ripple minimization control techniques exist, they rely on modeling the machine, which in turn requires specialized offline experimental setups or online (during operation) parameter identification techniques. To date, existing online techniques are iterative without proof of convergence, do not provide all model parameters, and/or rely on a priori information that can change after the machine is commissioned. In this work, an online parameter identification method is developed with a new empirical model of its flux linkage and electromagnetic torque, to provide a complete nonlinear model of the machine. With two seconds of data collected online, all electrical and mechanical parameters are identified using a non-iterative algorithm, and so it does not pose a risk of divergence. Therefore, parameter identification can be reliably and frequently carried out at different operating conditions as the machine ages for diagnostics. Also, the resulting model is designed to be used by advanced torque ripple minimization control techniques. The implementation procedure is detailed along with simulation results to demonstrate its efficacy.
Highlights
Modeling and parameter identification of electric machines is an important first step towards their reliable analysis and control
Several other switched reluctance machine (SRM) control methods were developed including the use of torque sharing functions in [10], [11], a hybrid PID torque sharing with τ -i-θ characteristics in [12], and the use of model predictive control and a Kalman filter with recursive least squares to estimate the inductances in [13]
An online parameter identification method was developed in this work for the SRM, which uses a new nonlinear empirical model of the machine
Summary
Modeling and parameter identification of electric machines (motors or generators) is an important first step towards their reliable analysis and control. Instead, closed-loop control aiming at torque ripple minimization is used, which requires knowledge of its model including the nonlinear flux linkage curves, and the induced electromagnetic torque in relation to the phase current and rotor position, i.e., the τ -i-θ characteristics. In many cases these models are obtained from experimental data to support such control techniques. Θ suffices for modeling both mechanical and electrical dynamics of the SRM [7]
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