Design Optimization for Total Volume Reduction of Permanent Magnet Synchronous Generators

الموضوعات : مهندسی هوشمند برق

Reza Ilka ¹ , Yousef Alinejad-Beromi ² , Hossein Asgharpour-Alamdari ³ , Hamid Yaghobi ⁴

1 - Electrical Engineering Department, Semnan University, Semnan, Iran.
2 - Electrical Engineering Department, Semnan University, Semnan, Iran.
3 - Electrical Engineering Department, Semnan University, Semnan, Iran.
4 - Electrical Engineering Department, Semnan University, Semnan, Iran.

تاريخ الإرسال : 24 الإثنين , جمادى الثانية, 1436 تاريخ التأكيد : 24 الإثنين , جمادى الثانية, 1436 تاريخ الإصدار : 22 السبت , رجب, 1434

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

ملخص المقالة :

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.

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