Indirect power control of DFIG based on wind turbine operating in MPPT using backstepping approach

Dbaghi, Yahya; Farhat, Sadik; Mediouni, Mohamed; Essakhi, Hassan; Elmoudden, Aicha

doi:10.11591/ijece.v11i3.pp1951-1961

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…This strategy identifies the appropriate control variables that cancel the error between the control and reference inputs. To ensure that, the reference x ref is set equal to the next sampling instant x k+1 ; additionally, the input corrector can be written as (14):…”

Section: Predictive Current Controlmentioning

confidence: 99%

“…Non-linear control methods based on Lyapunov's study [14], such as sliding mode control (SMC) [15] and backstepping [16], remain simple to implement and have robust compromise; on the other side, they provide some stability drawbacks or chattering phenomena [17]. In practice, the use of the SMC has been limited by the complicated online calculations and chattering problems related to the commutations of the control, which can be disturbing for the actuators.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Validation of Predictive Current Control for DFIG: FPGA Implementation

et al. 2021

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The purpose of this study is to design a real-time current predictive control for a wind energy conversion system (WECS) using a doubly-fed induction generator (DFIG). A wind emulator and a test bench for assessing control strategies were conceptualized. The DSPACE DS1104 board served as the foundation for the design of a wind emulation system. While power is indirectly regulated via currents, the latter is controlled directly by current predictive control. Using discrete time, the control suggests the appropriate voltages to the converter for each sample period to attain the specified set points and control the power. The field-oriented control is employed to ensure that the two components, axes d and q, are decoupled. The present predictive control was established to regulate a DFIG’s active and reactive capabilities. To begin, a thorough examination of the WECS is discussed. Following that, a comprehensive description of predictive control laws based on reference frame orientation is offered. As a result, a simulation was done using Matlab/Simulink environments to assess the performance and resilience of the proposed control model. The predictive current control was then experimentally validated on a test bench to demonstrate its efficacy. The observed results reveal an astonishing correlation between simulations and experiments.

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Section: Predictive Current Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Predictive Current Control for DFIG: FPGA Implementation

et al. 2021

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“…The stator and rotor voltages and field magnetic flux equations can be developed in the d-q park frame reference as follows [19]: (5) Where V s and V r are the stator and rotor voltage, i s and i r are the stator and the rotor current, φ s and φ r are the stator and the rotor flux linkages, R s and R r are the stator and the rotor resistances, L s , L r and M is the stator, rotor and magnetizing inductances, respectively.…”

Section: Dfig Modelingmentioning

confidence: 99%

“…The grid voltage is computed by the phase-locked loop (PLL) strategy. We hypothesize that the voltage across the stator resistance is neglected compared to stator terminal voltages [17][18][19]:…”

Section: Dfig Modelingmentioning

confidence: 99%

Hybrid Control using Adaptive Particle Swarm Optimization and Integral Backstepping Control of Grid-connected Doubly Fed Induction Generator

et al. 2021

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We proposed an analysis of a hybrid control of active and reactive power for a doubly-fed induction generator for variable velocity wind energy injection into the electrical grid using a combination of adaptive particle swarm optimization and integral backstepping control in this paper. The stability of the Lyapunov function is utilized to establish the latter. Six controllers are developed as part of the proposed control process: The first is concerned with the maximum PowerPoint. The stator powers are managed by the second and third regulators, which are performed by the optimal PI controller using adaptive particle swarm optimization. The DC link voltage is kept constant by the fourth controller. The fifth and sixth are employed to pilot the rotor powers and ensure that the power factor is maintained to 1. These three controllers are synthesized by using the nonlinear integral backstepping control. These control strategies show excellent results compared to field-oriented control under a variable wind speed profile and changing generator settings in a Matlab/Simulink environment. According to the test findings, using integral backstepping, the overshoot of the DC-link voltage is decreased by 99.16 %. Furthermore, the particle swarm optimization reduces its time to reach the equilibrium state to 4.3 m s and demonstrates robustness against parameter generator changes. HIGHLIGHTS The regulation of the produced power by the wind energy conversion system (WECS) based on a doubly-fed induction generator is becoming increasingly important to researchers. This system is modeled and simulated in the Matlab/Simulink software environment to apply the proposed control In order to extract the maximum power from the variable wind source, a maximum power point tracking method is developed based on the PI controller For piloting the wind energy system conversion (WECS) based on a DFIG, a combination of the integrated Backstepping controller and adaptive PSO is proposed and realized in this paper Robustness tests are established by adjusting the generator parameters, and a comparative study is conducted to verify the superiority of the suggested control over the indirect vector control GRAPHICAL ABSTRACT

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“…Sliding mode control with high order provides quick transient reaction and robustness against changes in internal machine parameters, as presented by the technological adaptive sliding mode control solution. The DFIG's backstepping control (BSC), which showed excellent flexibility to outside disruptions and provided a very-fast response time [8,9].…”

Section: Introductionmentioning

confidence: 99%

Current source converter-based optimal power extraction and power control of a doubly fed induction generator (DFIG) using backstepping control

Kumar,

Choudhary

2023

Eng. Res. Express

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An improved backstepping control technique for the operation of a doubly fed induction generator (DFIG) powered by a wind turbine (WT) was developed and discussed. It is derived from a current source converter (CSC). The optimal backstepping control (BSC) settings are determined using the CSC approach, which can improve the drive by providing a more rapid dynamic response, greater precision, and stable performance. The CSC approach control function was constructed using the index criterion integral time square error (ITSE) and integral time absolute error (ITAE). With the simulation technique in the MATLAB/Simulink environment, the proposed BSC-CSC effectiveness is confirmed, and analyse the performance by using the PI and SMC control. The results indicate the goals of this work have been achieved in the form of resilience, improved dynamic system efficiency, lowering the harmonic distortion, optimization of stator power, and successfully addressing the problem of model parameter uncertainty.

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Indirect power control of DFIG based on wind turbine operating in MPPT using backstepping approach

Cited by 9 publications

References 18 publications

Experimental Validation of Predictive Current Control for DFIG: FPGA Implementation

Experimental Validation of Predictive Current Control for DFIG: FPGA Implementation

Hybrid Control using Adaptive Particle Swarm Optimization and Integral Backstepping Control of Grid-connected Doubly Fed Induction Generator

Current source converter-based optimal power extraction and power control of a doubly fed induction generator (DFIG) using backstepping control

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