Design Optimization for Total Volume Reduction of Permanent Magnet Synchronous Generators

Authors

Electrical Engineering Department, Semnan University, Semnan, Iran.

Abstract

Permanent magnet synchronous generators (PMSGs) are novel generators which can be used in high-performance wind farms. High efficiency and flexibility in producing electricity from variable rotation make them good candidate for wind power applications. Furthermore, because these kinds of generators have no excitation winding, there is no copper loss on rotor; hence, they can operate at high power factor. Besides, performance characteristics of such generators could be further improved by design optimization. This paper presents design optimization of PMSGs used in small wind turbines using novel and efficient optimization algorithm i.e. Artificial Bee Colony (ABC) Algorithm. Then, a well-known optimization algorithm i.e. Genetic Algorithm (GA) is used to show the validity and efficiency of the before-mentioned algorithm. For this purpose, the necessary equations are provided. Objective function of this study is to reduce the total volume of motor. Case study of this study is a 5 kW, 220 V, 50 Hz, 100 rpm generator. Finally, results obtained by optimization are verified with Maxwell software which is based on finite element method (FEM). Comparison shows that the results of optimization approach are in good agreement with that of FEM ones.

Keywords


[1] H. Li, Z. Chen, H. Polinder, Optimization of Multibrid
Permanent-Magnet Wind Generator Systems, IEEE
transactions on energy conversion, vol.24, no. 1, pp. 82 – 92,
mar. 2009.
[2] J. Zhang, M. Cheng, Z. Chen, Optimal design of stator interior
permanent magnet machine with minimized cogging torque
for wind power application, energy conversion and
management vol. 49, no. 8, pp. 2100–2105, Aug. 2008.
[3] S. Kiartzis, A. Kladas, Deterministic and artificial intelligence
approaches in optimizing permanent magnet generators for
wind power applications, journal of materials processing
technology, vol. 108, no. 2, pp. 232–236, Jan. 2001.
[4] H. Li, Z. Chen, Design optimization and site matching of
direct-drive permanent magnet wind power generator systems,
renewable energy, vol. 34, no. 4, , pp. 1175–1184, Apr. 2009.
[5] M. Pinilla, S. Martinez, Optimal design of permanent-magnet
direct-drive generator for wind energy considering the cost
uncertainty in raw materials, Renewable Energy, vol. 41, pp.
267–276, May. 2012.
[6] T. Gundogdu, G. komurgoz, Technological and economical
analysis of salient pole and permanent magnet synchronous
machines designed for wind turbines, journal of magnetism
and magnetic materials, vol. 324, no. 17, pp. 2679–2686, Aug.
2012.
[7] G.M. Masters, Renewable and Efficient Electric Power
Systems, John Wiley & Sons, 1st edition ,Aug. 11.
[8] T. Ackermann, Wind Power in Power Systems, John Wiley &
Sons, Second edition, May. 2012.
[9] K. Von Frisch, Bees: Their Vision, Chemical Senses and
Language. (Revised edn) Cornell University Press,
N.Y.,Ithaca, 1976.
[10] K. Von Frisch, Bees: Their Vision, Chemical Senses and
Language. (Revised edn) Cornell University Press,
N.Y.,Ithaca, 1976.
[11] E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm
Intelligence: from Natural to Artificial Systems. Oxford
University Press, New York, 1999.
[12] S. Camazine, J. Deneubourg, NR. Franks, J. Sneyd, G.
Theraula and E. Bonabeau, Self-Organization in Biological
Systems. Princeton: Princeton University Press, 2003.
[13] A. P. Engelbrecht, Computational Intelligence: An
Introduction, John Wiley & Sons, 2007.