This paper presents a new fault diagnostic technique applied to switched reluctance motor drives, based on the analysis of the power converter supply current. A fault is detected when the measured amplitude of the dc bus current differs from its expected amplitude, assuming normal operating conditions. The information about phase currents amplitudes and the control commands of all power switches permit to easily estimate the amplitude of the power converter supply current, since an asymmetric bridge converter is used. Simulation and experimental results are presented. Open- and short-circuit fault occurrences in the converter power switches are considered and analyzed. The proposed technique can early detect these fault occurrences and can also identify the affected motor phase. In almost all situations, the faulty element is also identified. An early fault diagnosis, with an accurate fault identification, is of a paramount importance since it permits the early adoption of fault-tolerant procedures that minimize the fault impact on the machine operation.

A New Method for Power Converter Fault Diagnosis in SRM Drives

The paper presents a novel method of profiling the phase currents to minimize the torque ripple of a switched reluctance machine (SRM). The method is a combination of machine design and control algorithm. The minimization is accomplished through three steps. The first step is to design a machine which has symmetric torque characteristics with a maximum flat portion. The second step is to find the required current profile through simulation. The third step is fine tuning the profile through coupled simulation of the machine in the finite element and the dynamic controller in the Matlab/Simulink environments. The coupled simulation takes into consideration of the nonlinearities, electrical loss, magnetic loss, and mutual coupling.

Torque ripple minimization of switched reluctance machines through current profiling

The aim of this thesis is to estimate the perspectives of integrated switched reluctance drives (I-SRDs), i.e. reluctance machines integrated with converters. It is assumed that such drive series can be manufactured in the power range of 0.75...7.5 kW and speed ranges of 300...3000 rpm and 600...6000 rpm for applications like pumps, fans, conveyors, compressors, extruders and mixers. Based on the performed research and design work it is stated that the new drives have to be developed according to their applications, which determine objective functions and constraints, and that the best possible design should be found as a solution of a synthesis task. Sizing equations are not applied at all. The approach used in the thesis is based on the virtual prototyping concept, i.e. the new I-SRD series is designed in a virtual environment. Therefore, mathematical models and the ways to verify them have to be elaborated. The concepts of multidisciplinary and multilevel modeling are applied. The multidisciplinary model is a combination of interconnected electromagnetic, thermal and noise models. The multilevel concept is the approach when different elements of the drive are described using different languages, i.e. on different levels. Several original solutions are introduced, like the electromagnetic model comprising SIMULINK block-diagrams and MATLAB script, expressions for the correction of the flux linkage due to end-effects, an original equivalent circuit for thermal analysis, which allows using a very simple and fast method to solve the circuit, together with the concept of a multi-layer equivalent cylinder for modeling the motor winding. For verification of the multidisciplinary model a database of test results has been collected using both testing of several reluctance machines in the laboratory and analyzing of test results published by other researchers. After verification the model can be considered as a virtual prototype and can be used in the synthesis process. Several methods of solving the synthesis task were tested. The method, proved to be best suited for solving this task in the proposed form, is the genetic algorithm in the vector form with alphabetic encoding. The genetic algorithm should be coupled with the experimental design method or with the Monte-Carlo method.

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Development of methods, algorithms and software for optimal design of switched reluctance drives

Due to very high torque/weight ratio, the axial flux switched reluctance motor (AFSRM) can be appropriately used in many applications, especially electric vehicles and aerospace systems. Since the torque ripple is generally the drawback of SRMs, a new structure is suggested for the double-sided AFSRM in which the torque ripple is significantly reduced. In order to show the effectiveness of the technique proposed for reducing the torque ripple, the 3-D finite-element (FE) transient analysis of a typical 8/6 double-sided AFSRM is carried out using MAXWELL FE package and the simulation results are presented.

Modification in Geometric Structure of Double-Sided Axial Flux Switched Reluctance Motor for Mitigating Torque Ripple

This paper presents Particle Swarm Optimization (PSO) based design optimization of Switched Reluctance Machine (SRM). The SRM design is formulated as multiobjective constrained optimization problem. The objective functions for obtaining desired design are maximization of average torque and minimization of torque ripple with stator and rotor pole arc as design variables. The optimization procedure is tested on 8/6, four-phase, 5 HP, 1500 rpm SRM. The results are compared and investigated with those obtained from Genetic Algorithm (GA) technique and FEA simulation. The results demonstrate that the proposed method is effective and outperforms GA in terms of solution quality, accuracy, constraint handling and computational time.

Design optimization of Switched Reluctance Machine using Particle Swarm Optimization

This paper briefly describes an approach to determine the optimum magnetic circuit parameters to minimize low speed torque ripple for switched reluctance (SR) motors. For prediction of the torque ripple, normalized data obtained from field solution and a neural network approach is used. Comparison of experimental results with computations illustrates the accuracy of the method. The optimization method is briefly described and some results are presented.

/pdf/optimum-geometry-for-torque-ripple-minimization-of-switched-1ao6dosl4o.pdf

Funda Sahin

Papers

Optimum geometry for torque ripple minimization of switched reluctance motors