Optimal Design and Control of Doubly Fed Induction Generator via Field Reconstruction Method
Researchers at U.T. Dallas have developed a series of new strategies for Doubly Fed Induction Machine analysis, design and control. These new methods are based on the Field Reconstruction Method (FRM) which offers a field model of electrical machine rather than the lumped parameter models. FRM enables accurate, detailed modeling of electric machine, which in turn, yield better design and control performance. Case studies have been done in optimal design and torque pulsation mitigation of a Doubly Fed Induction Machine. Promising results were observed.
The continuous increase of fossil fuel demand has resulted in major energy and environmental challenge in recent years. Wind energy as a sustainable energy resource has obtained substantial attention among alternative energy resources. Doubly Fed Induction Generators are dominant in high power wind turbines. Traditional design and control of electric machines are usually based on lumped parameter circuit models of machine, which is based on enormous ideal assumptions. An equivalent circuit model without details of the machine potentially degrades the best control and design. Though FEA methods are available, its computational cost has limited its application in iterative design and optimization process. Maximum energy production and torque pulsation mitigation are selected as benchmark problems for Doubly Fed Induction Generators design and control.
Numerical iterative optimization, rather than evolutionary methods, are performed using commercial available software packages to generate the best parameters for machine and its control. Machine performance evaluation via Field Reconstruction Method offers an efficient way to model the electric machine with an acceptable accuracy compared with FEA.
Patent Pending with the University of Texas at Dallas.