Observer-Based Adaptive Backstepping Control of Grid-Connected Wind Turbine Under Deep Grid Voltage Dip

Adekanle, Oluwaseun Simon; Guisser, M.; Abdelmounim, Elhassane; Mohamed, Azah

doi:10.1007/978-3-030-05276-8_9

Cited by 9 publications

(14 citation statements)

References 15 publications

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“…The schematic of our control strategy is given in Figure 5. To verify the robustness of this proposed controller, PSO-Backstepping is compared with arbitrary backstepping controller which is implemented on recent works [26][27][28][29]. Figure 4 shows the simulation results without parameters variations and the zoom of these resultants, respectively.…”

Section: Results and Simulationmentioning

confidence: 99%

PSO-backstepping controller of a grid connected DFIG based wind turbine

Salarieh¹,

Badri²,

Zegrari³

et al. 2020

IJECE

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The paper demonstrates the feasibility of an optimal backstepping controller for doubly fed induction generator based wind turbine (DFIG). The main purpose is the extract of maximum energy and the control of active and reactive power exchanged between the generator and electrical grid in presence of uncertainty. The maximum energy is obtained by applying an algorithm based on artificial bee colony approach. Particle swarm optimization is used to select optimal value of backstepping's parameters. The simulation is carried out on 2.4 MW DFIG based wind turbine system. The optimized performance of the proposed control technique under uncertainty parameters is established by simulation results.

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Section: Results and Simulationmentioning

confidence: 99%

PSO-backstepping controller of a grid connected DFIG based wind turbine

Salarieh¹,

Badri²,

Zegrari³

et al. 2020

IJECE

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“…In recent years, several nonlinear control approaches have been suggested. One of the most major strategies in the literature [21][22][23][24][25][26][27] is the backstepping technique. The basic idea of the backstepping strategy is to stabilize the system from the first subsystem by stabilizing the defined function through a chosen Lyapunov function [23].…”

Section: Backstepping Control Of Dfigmentioning

confidence: 99%

“…The goal is to obtain zero errors convergence, therefore stabilizing and balancing the system which enables the output to track a reference [25]. Therefore, two backstepping controls are utilized in this work to regulate the active and reactive power of the DFIG, as contrary to some researchers [23,24,26,27], who utilize more than two controllers (three or four controllers).…”

Section: Backstepping Control Of Dfigmentioning

confidence: 99%

“…Otherwise, several approaches have been suggested in the kinds of literature to clarify the efficiency of backstepping control [21][22][23][24][25][26][27]. The authors in [22] use an effective nonlinear backstepping control strategy of a WECS based on a DFIG in order to ensure the system's overall stability.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, to enhance the efficiency of the WECS based on a permanent magnet synchronous generator attached to the network, the authors successfully implemented nonlinear backstepping control on a real system in [23]. Moreover, in order to optimize the performance of grid-connected DFIG, the authors in [24], suggest the combination of an integral backstepping control with the high-gain observer. In [25], the authors proposed an algorithm that mixes the nonlinear backstepping strategy with the FOC for controlling the active and reactive power of the DFIG, as well as regulating the energy transferred to the power network.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Backstepping Control of a DFIG based Wind Energy Conversion System using Ant Lion Optimizer Algorithm

Zeghdi

Barazane

Bekakra

et al. 2022

Period. Polytech. Elec. Eng. Comp. Sci.

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In this paper, an improved Backstepping control based on a recent optimization method called Ant Lion Optimizer (ALO) algorithm for a Doubly Fed Induction Generator (DFIG) driven by a wind turbine is designed and presented. ALO algorithm is applied for obtaining optimum Backstepping control (BCS) parameters that are able to make the drive more robust with a faster dynamic response, higher accuracy and steady performance. The fitness function of the ALO algorithm to be minimized is designed using some indexes criterion like Integral Time Absolute Error (ITAE) and Integral Time Square Error (ITSE). Simulation tests are carried out in MATLAB/Simulink environment to validate the effectiveness of the proposed BCS-ALO and compared to the conventional BCS control. The results prove that the objectives of this paper were accomplished in terms of robustness, better dynamic efficiency, reduced harmonic distortion, minimization of stator powers ripples and performing well in solving the problem of uncertainty of the model parameter.

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MPPT Performance and Power Quality Improvement by Using Fractional-Order Adaptive Backstepping Control of a DFIG-Based Wind Turbine with Disturbance and Uncertain Parameters

Kasbi

Rahali

2022

Arab J Sci Eng

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Observer-Based Adaptive Backstepping Control of Grid-Connected Wind Turbine Under Deep Grid Voltage Dip

Cited by 9 publications

References 15 publications

PSO-backstepping controller of a grid connected DFIG based wind turbine

PSO-backstepping controller of a grid connected DFIG based wind turbine

Improved Backstepping Control of a DFIG based Wind Energy Conversion System using Ant Lion Optimizer Algorithm

MPPT Performance and Power Quality Improvement by Using Fractional-Order Adaptive Backstepping Control of a DFIG-Based Wind Turbine with Disturbance and Uncertain Parameters

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