Abstract
Problem statement: Switched Reluctance Motors (SRMs) are widely used in various applications due to their inherent simplicity and rugged construction In SRM, torque output and torque ripple are sensitive to stator and rotor pole arcs and their selection is a vital part of SRM design process. In this study Particle Swarm Optimization technique is proposed for determining optimum pole arc of SRM. Approach: The problem of determining optimum pole arc is formulated as a multiobjective optimization problem with the objective of maximizing average torque and minimizing torque ripple. A comprehensive program based on analytical model is developed in Matlab to compute the value of inductance and average torque. Results: 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 Finite Element Analysis(FEA) simulation. Conclusion: The results demonstrate that the proposed method is effective and outperforms GA in terms of solution quality, accuracy, constraint handling.
Highlights
Simple and robust structure, high efficiency and fault tolerability of Switched Reluctance Machine (SRM) are good reasons for its selection in variable speed applications (Lawrenson et al, 1980)
Generalized regression neural network based optimization of SRM with the objective of maximizing average torque and minimizing torque ripple is discussed in (Sahraoui, et al, 2007).Optimization techniques like Genetic Algorithm and Taguchi algorithm have been applied for switched reluctance machine design(Kano et al, 2010;Mirzaeian et al, 2002; Nabeta et al, 2008)
From the literature it is evident that computational intelligence techniques like genetic algorithm and artificial neural network have been successfully applied for design optimization of SRM
Summary
High efficiency and fault tolerability of Switched Reluctance Machine (SRM) are good reasons for its selection in variable speed applications (Lawrenson et al, 1980). For optimal design of the SR motor by evolutionary algorithms such as PSO, a large number of performance evaluations are required and the computational time by analytical method would be very large. To have a practically feasible and acceptable final design the following performance constraints are imposed: data which were not subjected to training This network is incorporated in the optimization routine to compute the value of average torque. More interesting PSO advantages can be emphasized when compared to other members of evolutionary algorithms like: particle’s movement; and w is the inertia weight and it It can be programmed and modified with keeps a balance between exploration and exploitation In our case, it is a linearly decreasing function of the inexpensive in terms of computation time and iteration index: memory w(k). Stator Pole arc Rotor Pole arc Average Torque Inductance ratio Torque dip Initial design
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