Abstract
This paper proposes an optimal gain-scheduling for linear quadratic regulator (LQR) control framework to improve the performance of wind turbines based Doubly Fed Induction Generator (DFIG). Active and reactive power decoupling is performed using the field-oriented vector control which is used to simplify DFIG’s nonlinearity and derive a compact linearized state-space model. The performance of the optimal controller represented by a linear quadratic regulator is further enhanced using the whale optimization algorithm in a multiobjective optimization environment. Adaptiveness against wind speed variation is achieved in an offline training process at a discretized wind speed domain. Lookup tables are used to store the optimal controller parameter and called upon during the online implementation. The control framework further integrates the effects of pitch angle control mechanism for active power ancillary services and possible improvements on reactive power support. The results of the proposed control framework improve the overall performance of the system compared to the conventional PI controller. Comparison is performed using the MATLAB Simulink platform.
Highlights
Electricity is one of the life necessities that made a quantum leap in the world
To overcome drawbacks of PI control mechanism manifested in difficult dynamic analysis for multiple-input and multiple-output systems and poor performance at parameter variations, an optimal controller is utilized with LQR in [36, 37] and with multivariable Hinf controller in [38]. e authors in [39] present a new control strategy of the Doubly Fed Induction Generator (DFIG) based wind turbine operating at one wind speed using the linear quadratic regulator (LQR) with optimal weighting matrices chosen based on Genetic Algorithm. e main objective is to improve the dynamic response, reliability, and stability of the system
A convergence diagram is provided in Figure 8 for the objective function and the individual indices at wind speed of 8 m/s
Summary
Electricity is one of the life necessities that made a quantum leap in the world. Traditionally, electrical energy is primarily produced using fossil fuel energy resources. E authors in [39] present a new control strategy of the DFIG based wind turbine operating at one wind speed using the linear quadratic regulator (LQR) with optimal weighting matrices chosen based on Genetic Algorithm. E time derivative of stator and rotor currents can be extracted by rearranging the previous model as demonstrated in equations (15)–(18), where σ 1 − L2m/LrLs. Evaluating voltages and currents in the synchronous reference frame enables calculating the active and reactive power for both the stator and the rotor (19)–(22). E equations are derived by rearranging the relations obtained previously for the DFIG machine, the grid-side filter, the wind turbine mechanical model, and the DC link. Because of the alignment made with the stator flux space vector, rotor current components can provide the ability to independently control stator real and reactive power as demonstrated in (55) and (56). Damping can be quantified by dividing the pole’s imaginary parts over the poles’ real parts. us, idro,iqro,idgo,iqgo,wrmo,vdco
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