This paper discusses how the switched reluctance generator (SRG) converts energy as directed by a controller. Beginning with a review of the electromechanics of generation, the paper identifies the implications of the energy conversion process on how the SRG is controlled. The structure of the SRG controller for speed-control and power-control applications is discussed. Practical implementation details for commutation of the SRG are reviewed. Concepts are illustrated with a 6-kW SRG designed to serve as a starter/alternator in automotive applications.

This paper presents a new approach to the automatic control of excitation parameters for the switched-reluctance generator (SRG) where the SRG system operates at sufficiently high speed that it operates in the single pulse mode. The turn-on and turn-off angles are the two parameters through which we can control the electric power generation. The objective of the work is to develop an easily implementable control algorithm that automatically maintains the most efficient excitation angles in producing the required amount of electric power. The work is focused on finding the most efficient excitation angles and characterizing them for easy implemention under closed loop control. Through modeling of an experimental SRG and extensive simulation, it can be seen that the optimal-efficiency turn-off angles can be characterized as a function of power and speed level. Within the closed-loop power controller, the optimal-efficiency turn-off angle is determined from an analytic curve fit. The turn-on angle is then used as the degree of freedom necessary to regulate the power produced by the SRG. Given that the turn-off angle is associated with optimal-efficiency at each speed and power point, overall operation is achieved at optimal-efficiency. The SRG, inverter and control system are modeled in Simulink to demonstrate the operation of the system when implemented within a voltage regulation system. The control technique is then applied to an experimental SRG system. Experimental operation documents that the technique provides for efficient operation of the SRG system through tuning the controller at only four operating points.

No abstract

This paper presents a new approach to the sensorless control of the switched-reluctance motor (SRM). The basic premise of the method is that an artificial neural network (ANN) forms a very efficient mapping structure for the nonlinear SRM. Through measurement of the phase flux linkages and phase currents the neural network is able to estimate the rotor position, thereby facilitating elimination of the rotor position sensor. The ANN training data set is comprised of magnetization data for the SRM with flux linkage ( ) and current ( ) as inputs and the corresponding position ( ) as output in this set. Given a sufficiently large training data set, the ANN can build up a correlation among and for an appropriate network architecture. This paper presents the development, implementation, and operation of an ANN-based position estimator for a three-phase SRM.

No abstract