This research work presents a new and efficient design methodology for the specification, development and manufacture of permanent magnet synchronous motors (PMSMs). In this paper a genetic algorithm based design optimisation technique for PMSMs is presented in which the multicriteria
considered in the optimisation are the electromagnetic performance, the thermal performance and the material cost. Models have been developed for each criterion in order to calculate the objective vector. A software tool called PMSM Analyser was developed to assist the motor design methodology. The optimisation algorithms and the electromagnetic, thermal and cost
models were integrated and interfaced using this software. The programme is demonstrated
for the design of a 12 slot 10 pole PMSM. The design parameter vector contains stator bore diameter, stator tooth thickness and stator back iron thickness. For the base design the outer diameter of the stator is 180mm and the stack length of the motor is 90mm. The base design refers to the design before optimisation and the optimal design refers to the design with optimised dimensions. The optimisation programme predicts significant improvements over the baseline design and experimental results are presented which indicate good agreement with the predictions of the programme. The new approach has been used successfully in
the development and design of a PMSM with a stall torque of 125Nm, rated torque of 75Nm at 1500r/min and output power of 12kW. The strengths of the design methodology are summarised with the genetic algorithm optimisation, innovative multi-objective handling and design models for the
various disciplines of PMSM development.
[1]
Michael P. Fourman,et al.
Compaction of Symbolic Layout Using Genetic Algorithms
,
1985,
ICGA.
[2]
Kalyanmoy Deb,et al.
A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
,
1990,
FOGA.
[3]
Peter J. B. Hancock,et al.
An Empirical Comparison of Selection Methods in Evolutionary Algorithms
,
1994,
Evolutionary Computing, AISB Workshop.
[4]
Thomas Bäck,et al.
Optimal Mutation Rates in Genetic Search
,
1993,
ICGA.
[5]
David E. Goldberg,et al.
Genetic Algorithms in Search Optimization and Machine Learning
,
1988
.
[6]
John H. Holland,et al.
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
,
1992
.
[7]
Lawrence J. Fogel,et al.
Artificial Intelligence through Simulated Evolution
,
1966
.
[8]
Carlos M. Fonseca,et al.
Multiobjective genetic algorithms with application to control engineering problems.
,
1995
.
[9]
D. E. Goldberg,et al.
Genetic Algorithms in Search
,
1989
.