Design Optimization for Total Volume Reduction of Permanent Magnet Synchronous Generators

محورهای موضوعی : مهندسی هوشمند برق

Reza Ilka ¹ ( Electrical Engineering Department, Semnan University, Semnan, Iran. )
Yousef Alinejad-Beromi ² ( Electrical Engineering Department, Semnan University, Semnan, Iran. )
Hossein Asgharpour-Alamdari ³ ( Electrical Engineering Department, Semnan University, Semnan, Iran. )
Hamid Yaghobi ⁴ ( Electrical Engineering Department, Semnan University, Semnan, Iran. )

تاریخ دریافت : 1394/01/24 تاریخ پذیرش : 1394/01/24 تاریخ انتشار : 1392/03/11

کلید واژه: Wind Farms, Permanent Magnet Synchronous Generator (PMSG), Design optimization, Artificial Bee Colony (ABC) Algorithm, Genetic Algorithm (GA), Finite element method (FEM), Ansoft Maxwell,

چکیده مقاله :

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.

منابع و مأخذ:

[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.