The matrix converter is a new generation of power electronic converters and is an alternative to back-to-back converters in applications that dimensions and weight are important. In this paper, a simple control algorithm for a three-phase asynchronous motor based on a direct torque control technique, which is fed through a three-phase direct matrix converter, is presented. For direct matrix converters, 27 switching modes are possible, which using the predictive control technique and for the different modes of the matrix converter, the motor behavior is estimated at the next sampling interval. Then the objective function is determined and the optimal possible mode is selected. Finally, the best switching mode is applied to the direct matrix converter. In order to evaluate the proposed method, simulation of the system in Matlab/Simulink software environment is performed. The results show the effectiveness of the proposed method.
In an effort to achieve an optimal availability time of induction motors via fault probabilities reduction and improved prediction or diagnostic tools responsiveness, a conditional probabilistic approach was used. So, a Bayesian network (BN) has been developed in this paper. The objective will be to prioritize predictive and corrective maintenance actions based on the definition of the most probable fault elements and to see how they serve as a foundation for the decision framework. We have explored the causes of faults for an induction motor. The influence of different power ranges and the criticality of the electric induction motor are also discussed. With regard to the problem of induction motor faults monitoring and diagnostics, each technique developed in the literature concerns one or two faults. The model developed, through its unique structure, is valid for all faults and all situations. Application of the proposed approach to some machines shows promising results on the practical side. The model developed uses factual information (causes and effects) that is easy to identify, since it is best known to the operator. After that comes an investigation into the causal links and the definition of the a priori probabilities. The presented application of Bayesian networks is the first of its kind to predict faults of induction motors. Following the results of the inference obtained, prioritizations of the actions can be carried out.