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

Salarieh, Hassan; Badri, Abdelmajid; Zegrari, Mourad; Sahel, Aïcha; Baghdad, Abdennaceur

doi:10.11591/ijece.v10i1.pp856-867

Cited by 9 publications

(13 citation statements)

References 11 publications

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“…In 17- (19) modelled the best positions of α β and δ Wolves while (20) represents the updating of positions of other Wolves from the best positions of α β and δ Wolves [29]. The A and C are variable vectors [30] presented in (21) and (22). If the value of vector A is greater than 1 or less than -1, the wolves leave the candidate solution and search for a better one.…”

Section: Grey Wolf Optimizer Tuning Methodsmentioning

confidence: 99%

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Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer

Sule

Mokhtar

Jamian

et al. 2020

IJECE

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The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the PSO and GA tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and the controller step input response. The GWO, the Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA) tuning methods were implemented in the Matlab 2016b to search the optimal gains of the Proportional and Integral controller through minimization of the objective function. A comparison was made between the results obtained from the GWO tuning method against PSO and GA tuning techniques. The GWO computed the smallest value of the objective function minimized. It exhibited faster convergence and better time response specification compared to other methods. These and more performance indicators show the superiority of the GWO tuning method.

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Section: Grey Wolf Optimizer Tuning Methodsmentioning

confidence: 99%

“…It is applied to the generator input for producing electrical power. The models representing the wind turbine mechanical power and torque [20][21][22][23] (1)…”

Section: Wind Turbine Modelmentioning

confidence: 99%

Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer

Sule

Mokhtar

Jamian

et al. 2020

IJECE

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“…Some of these merits are the capability to capture maximum power and reducing the mechanical stress on the drive-train [2,3]. There are various types of electrical generator used in the VSWG, such as switched reluctance generator (SRG) [4], a doubly-fed induction generator (DFIG) [5,6], and permanent magnet synchronous generator (PMSG) [7][8][9][10][11][12][13]. The PMSGs have many advantages, for example, they have high number of poles, therefore there are power can be achieved at low rotational speed and there is no needing for gearbox resulting in reducing cost.…”

Section: Introductionmentioning

confidence: 99%

Hybrid PSO-GSA algorithm-based optimal control strategy for performance enhancement of a grid-connected wind generator

Amin

Soliman

Hasanien

et al. 2021

IJAPE

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<span>Due to the great level of wind energy penetration in the existing network, huge efforts have been directed to enhance the grid-connected wind generator performance. This paper shows an application of a hybrid algorithm of the particle swarm optimization and the gravitational search algorithm (PSO-GSA) to enhance the transient stability of the grid-tied wind energy conversion system. The variable-speed wind turbine (VSWT) direct-drive permanent-magnet synchronous generator is connected to the network through a full-scale converter. The generator- and grid-side converters are controlled by utilizing an optimum proportional-integral (PI) controller. The criterion of the integral squared error is utilized as an objective function. The PSO-GSA based-PI controller efficacy is validated by comparing its results with that are obtained by utilizing the genetic algorithm (GA)-based-PI controller. The performance of the suggested control scheme is checked during various fault conditions. The control scheme quality is legalized by the simulation results that are obtained using MATLAB/Simulink program</span>

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“…The wind estimator and the control law were verified using MATLAB/Simulink as well. Also, a backstepping robust controller for a 2.4 MW DFIG wind turbine was obtained in case of technical disturbances in the generator, along with the application of PSO to determine the optimum parameters [10].…”

Section: Introductionmentioning

confidence: 99%

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

Al‐Gabalawy

Hosny

Hussien

2020

IJECE

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Wind Energy has received great attention in this century. It influences the new power systems, adding new challenges to the power system expansion problem. Nowadays, double feed induction generator (DFIG) wind turbines are used majorly in wind farms, due to their advantages over other types. Therefore, the analysis of the system using this type has become very important. In this paper, a wind turbine modelling was introduced with suggested controllers, in order to enhance the system response, with respect to both pitch control and maximum output power. Cuckoo search algorithm (CSA), a meta-heuristic optimization technique, was implemented to determine the gains of a proportional-integral (PI) controller and fractional order proportional-integral-derivative (FOPID) controller to optimize the system, which considered three control loops: pitch, rotor-side converter, and grid-side converter control loop. Simulation results were determined using MATLAB/Simulink. The comparative analysis of the results showed that the PI Controller gave the simplest and the best response in case of the pitch and rotor-side control loops while the FOPID was the best when applied to the grid-side control loop. Based on the results and discussion, a suggestion of using a compination of each controller was introduced.

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

Cited by 9 publications

References 11 publications

Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer

Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer

Hybrid PSO-GSA algorithm-based optimal control strategy for performance enhancement of a grid-connected wind generator

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

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