Design and implementation of optimal controller for DFIG-WT using autonomous groups particle swarm optimization

Seoudy, H. M.; Saadeldin, Mohamed Attya; Abdelfattah, Wael

doi:10.11591/ijpeds.v13.i3.pp1813-1821

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…So, each particle reaches the optimal solution or gets as close to it as possible. The position and velocity of the conventional PSO are defined as [23]- [25].…”

Section: Conventional Psomentioning

confidence: 99%

Enhancing the output power of solar cell system using artificial intelligence algorithms

Ali,

El-Kammar,

Hamed

et al. 2024

IJPEDS

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The main objective of research in the field of solar cell systems is to obtain the maximum output power. In this respect, artificial intelligence (AI) is considered the current icon. Hence, in the present paper perturbation & observation (P&O) and particle swarm optimization (PSO) algorithms were used to achieve the maximum power. Solar irradiance at three different regions of Egypt was measured using a new technique based on Arduino microcontroller. The obtained experimental results of the solar irradiance were inlaid to the MATLAB simulation program to study the performance of the proposed algorithms. Many improvements were carried out in P&O and PSO algorithms to harvest maximum power for long hours daily by a continuous modulation of the duty cycle. The output maximum power and the reaching time of both improved P&O and PSO are better than the traditional one and PV array, which indicates their efficiency in harvesting the maximum power and enhancing the performance of solar cell systems. The reinforcing of the PV system by P&O improved its efficiency by 98.733%, while PSO improved its efficiency by 99.968%.

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“…So, each particle reaches the optimal solution or gets as close to it as possible. The position and velocity of the conventional PSO are defined as [23]- [25].…”

Section: Conventional Psomentioning

confidence: 99%

Enhancing the output power of solar cell system using artificial intelligence algorithms

Ali,

El-Kammar,

Hamed

et al. 2024

IJPEDS

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show abstract

“…Eberhart and Kennedy were the first to develop PSO, which is used to optimize continuous non-linear functions. The 1363 simplicity of implementation and the absence of a need for gradient information are two appealing aspects of PSO [22]- [25]. Numerous various optimization issues may be resolved using it.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Speed control of brushless DCmotors using (conventional, heuristic, and intelligent) methods-based PID controllers

Shary

Nekad

Alawan

2023

IJEECS

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One of the most often utilized types of direct current (DC) motors in both the industrial and automotive sectors are brushless DC motors (BLDC). This research presents a comparative analysis on brushless DC motor speed management. A mathematical model of the BLDC motor is developed using MATLAB/Simulink, and its speed is tested using three alternative controller types. The first controller is a traditional proportional integral derivative (PID) controller for BLDC motor speed control. The second controller used the particle swarm optimization (PSO) approach with PID which give the best response for BLDC motor speed. The PID controller in the third method based on neural network also give best reaction on motor speed. Finally, comparison made in speed and torque profiles by using sudden changes in speed and load torque under the three proposed methods. The results show when using first controller the speed rise to 1,526 r.p.m and drop to 1,400 r.p.m at the test conditions. These oscillations will disappear when using the second and third controller.

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“…Indeed, the quadratic current reference value is deduced from the external velocity loop [25], [26]. The optimal reference speed is deduced from the MPPT characteristic, which describes the evolution of power as a function of turbine speed for different values of wind speed [27].…”

Section: Dfig Controlmentioning

confidence: 99%

Comparative study between PI and SMC controllers for DFIG using fuzzy wind power estimator

Jemmali,

Elleuch,

Abid

et al. 2023

IJPEDS

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In this paper, we are interested in the control of the rotor current of the doubly feed induction generator (DFIG), that is used in wind turbines. In this context, we present a comparative study between two types of controllers. The first is based on a classic PI proportional-integral control, while the second is based on the SMC sliding control. The command chain uses a fuzzy estimator of the wind power, which makes it possible to determine the reference speed of DFIG. The system includes essentially a turbine, two converters, one on the rotor side and the other on the grid side, and a filter. The grid-side converter is used to maintain a constant DC bus voltage and to control rotor reactive power. The rotor side converter is used to control the speed of the DFIG to guarantee maximum power extracted from the turbine. In addition, this converter allows controlling the reactive power through the control of the rotor current according to the axis "d" of the frame linked to the rotating field. The simulation results show the effectiveness of sliding mode control (SMC) for the considered system.

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Design and implementation of optimal controller for DFIG-WT using autonomous groups particle swarm optimization

Cited by 4 publications

References 13 publications

Enhancing the output power of solar cell system using artificial intelligence algorithms

Enhancing the output power of solar cell system using artificial intelligence algorithms

Speed control of brushless DCmotors using (conventional, heuristic, and intelligent) methods-based PID controllers

Comparative study between PI and SMC controllers for DFIG using fuzzy wind power estimator

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