This paper presents a new methodology for alternator design using Particle Swarm Optimization (PSO) method to minimize the material cost and regulation and to maximize the efficiency. The design problem of an alternator is considered a nonlinear, multivariable constrained optimization problem. In this context, the design is optimized using three different objective functionscost, regulation and efficiency. The design procedure consists of a system of nonlinear equations, which defines alternator characteristics, performance, magnetic stresses and thermal limits. This procedure employs PSO technique to search for optimal values of machine variable without constraint violation. To verify the consistency, the proposed method has been applied to two sample alternators and the results are compared with that of Hooke-Jeeves (HJ) method. Computational results show the effectiveness of the proposed design.

Economic considerations in the design of an alternator were introduced in the early years of electric machine design. However, full utilization of the materials was possible only with the advent of the digital computers and the development of optimization techniques. The optimal design of an alternator for maximum efficiency or minimum cost, using mathematical optimization techniques, is an appropriate approach to an alternator design. With this approach, any desired requirement of either the manufacturer or the consumer may be expressed easily in the form of the optimization problem. The optimal design parameters can be derived by solving a constrained optimization problem, which belongs to a family of `general nonlinear programing problems'. The problem consists of an objective function which is minimized (cost, regulation) or maximized (efficiency) with a set of constraints.

The design procedure for the three-phase hetero-polar type of inductor alternator has been worked out (Pradiptak et al., 1989) and the stator leakage reactance which is an important parameter in the design and operation of such alternators has been reviewed. Spooner and Williamson (1996) have designed and constructed a multipole radial flux permanent magnet test machine for use as a direct coupled generator in wind turbines. A new technique for the optimal design of the surface permanent magnet synchronous motor considering the parameter correction of synchronous reactance has been presented (Jang and Joong, 1999). The advanced immune algorithm was used in the optimization procedure. The design of outer rotor (the positions of the rotor and stator are exchanged) radial flux permanent magnet multipolar low speed directly coupled wind power convertor for stand-alone applications has been presented (Chen et al., 2000). A3 KVA, 28 V permanent magnet brushless alternator for light combat aircraft has been designed and analyzed (Comanescu et al., 2003).

Russenschuck (1990) demonstrated an optimization technique for a synchronous permanent motor by minimizing the magnet volume and the harmonic vector with a Lagrange penalty function used to satisfy the power requirement. Andersen (1991) suggested one of the more robust approaches for complex optimization. This technique used Monte Carlo based search directions in the parameter space and included complex factors, such as short circuit reactance and thermal constraints, by the use of penalty functions. Analytical approximations had to be employed for these complex functions. Gu et al. (1994) have investigated the design optimization of permanent magnet generator for different magnet dimensions and airgap lengths. Numerical design of synchronous generators has been presented (Kent and Bruce, 2005). This method both computes the synchronous reactance under heavy saturation and integrates it into the machine design. An analytical algorithm was developed for the permanent magnet brushless alternator and the finite element analysis has been carried out for refining the design and performance (Bhim and Jally, 2006).

Electrical and Electronics Engineering Journal, Swarm Optimization Technique, Hooke-Jeeves Method, Particle Swarm Optimization, Mathematical Optimization Techniques, Multipole Radial Flux Permanent Magnet, Thermal Constraints, Analytical Approximations, Finite Element Analysis, Magnetic Loading, Animal Societies, Biological Inspiration, Cost Optimization, Electromagnetic Devices.