IEEE Transactions on Energy Conversion

This paper presents a primary-flux-control algorithm suitable for applications in short-primary long-secondary linear induction motor-drive systems. The advantage of the proposed control algorithm is that it is more robust than the conventional vector control algorithm to the variation of the secondary resistance caused by the effect of a joint in the secondary conductors, which can be made of several pieces of aluminum plates or rails joined together. The algorithm was tested by simulation on a sample of linear motor disk-drive system. The simulation results were compared to those obtained by a conventional vector control algorithm. Simulations and experiments have shown that the fluctuations of the thrust and attraction forces obtained with the proposed primary-flux control algorithm were significantly less than those obtained with the conventional vector control algorithm.

Experimental results on the magnetic saturation effects on the control of a small synchronous reluctance machine (600 W) are presented in this paper. A new model of the machine, including saturation effects and cross magnetization is first developed. An approach based on total and mutual inductances is followed instead of the traditional approach through magnetizing and leakage inductances. All of the electrical and mechanical parameters measurements required for the simulations are given. A rotor-oriented vector control of the synchronous reluctance machine is achieved with a digital-signal-processor (DSP) board (TMS320C31) and experimental results are presented. When the magnetic circuit is saturated, the simulation with the developed model shows good, accurate results when compared to the experimental ones.

This paper presents a procedure for increasing the daily output mechanical energy supplied by DC motors directly connected to solar cell arrays (SCA). The technique is based on the use of DC motors with two components of magnetic field: a constant component that can be provided by a permanent magnet (or a separately excited winding) and a second one produced by a series field winding. It will be shown that through properly selecting the parameters of the motor's magnetic circuit, (i.e., the value of the constant field and the mutual inductance as well as their relative directions), an improved matching between the PV-array and the dc motor can be achieved. Based on assessing and minimizing an expression used to quantify the power mismatch between the SCA and the dc motor, the optimal parameters will be identified. A comparison will then be made between this suggested motor design and tooth the separately excited and series motors.

This paper examines the feasibility of using artificial neural networks (ANNs) and genetic algorithms (GAs) to develop discrete time dynamic models for fault free and faulted switched-reluctance-motor (SRM) drive systems. The results of using the ANN-GA-based (neurogenetic) model to predict the performance characteristics of a prototype SRM drive motor under normal and abnormal operating conditions are presented and verified by comparison to test data.