Proposed particle swarm optimization technique for the wind turbine control system

Iqbal, Atif; Ying, Deng; Saleem, Adeel; Hayat, Muhammad Aftab; Mateen, Muhammad

doi:10.1177/0020294020902785

Cited by 21 publications

(14 citation statements)

References 27 publications

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“…The parameters of the simulated PMGS-based WT system and PSO algorithm are given in the Tables 1 and 2 (Asgharnia et al, 2018; Dorrah et al, 2012; Iqbal et al, 2020).…”

Section: Results Of Calculationmentioning

confidence: 99%

Optimized fuzzy fractional PI-based MPPT controllers for a variable-speed wind turbine

2022

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In recent years, wind power has become one of the most popular sources of renewable electricity generation. However, the wind is, by its nature, a highly intermittent source of energy. To capture the maximum power, wind turbines are generally equipped with a Maximum Power Point Tracking (MPPT) Controller. This paper proposes effective and robust MPPT control strategies based on Fuzzy Logic controller PI (FPI) and Fuzzy logic Fractional Order controller PI (FFOPI). Particle Swarm Optimization (PSO) is used to optimize the membership functions of FPI and FFOPI. The proposed MPPT strategies are validated on a Permanent Magnet Synchronous Generator (PMSG)-variable-speed wind energy conversion system. The overall model of the wind turbine-PMSG and control scheme is developed in MATLAB/Simulink and SimPower Systems toolbox. The results show that the MPPT based on FFOPI control optimized by PSO leads to the best transient response performance and robustness.

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“…The parameters of the simulated PMGS-based WT system and PSO algorithm are given in the Tables 1 and 2 (Asgharnia et al, 2018; Dorrah et al, 2012; Iqbal et al, 2020).…”

Section: Results Of Calculationmentioning

confidence: 99%

Optimized fuzzy fractional PI-based MPPT controllers for a variable-speed wind turbine

2022

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“…Particle swarm optimization (PSO) is a population-based, stochastic evolutionary computer technique for problem-solving [54]. It is a type of swarm intelligence, based on socio-psychological concepts, that gives insights into social behavior while also contributing to technical applications.…”

Section: Pso Overviewmentioning

confidence: 99%

Stability Enhancement of Wind Energy Conversion Systems Based on Optimal Superconducting Magnetic Energy Storage Systems Using the Archimedes Optimization Algorithm

et al. 2022

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Throughout the past several years, the renewable energy contribution and particularly the contribution of wind energy to electrical grid systems increased significantly, along with the problem of keeping the systems stable. This article presents a new optimization technique entitled the Archimedes optimization algorithm (AOA) that enhances the wind energy conversion system’s stability, integrated with a superconducting magnetic energy storage (SMES) system that uses a proportional integral (PI) controller. The AOA is a modern population technique based on Archimedes’ law of physics. The SMES system has a big impact in integrating wind generators with the electrical grid by regulating the output of wind generators and strengthening the power system’s performance. In this study, the AOA was employed to determine the optimum conditions of the PI controller that regulates the charging and discharging of the SMES system. The simulation outcomes of the AOA, the genetic algorithm (GA), and particle swarm optimization (PSO) were compared to ensure the efficacy of the introduced optimization algorithm. The simulation results showed the effectiveness of the optimally controlled SMES system, using the AOA in smoothing the output power variations and increasing the stability of the system under various operating conditions.

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“…The use of these smart algorithms leads to a significant improvement in the efficiency of the systems. In Iqbal et al, 38 the PSO algorithm is used to calculate the PI controller parameters of a wind turbine control system based on DFIG to improve the current quality. Simulation was used to implement this intelligent control with a comparison of the results with the traditional control that depends on the PI, where the results showed that the use of the PSO algorithm leads to a significant improvement in the characteristics of the system compared to the DPC.…”

Section: Introductionmentioning

confidence: 99%

Fractional-order neural control of a DFIG supplied by a two-level PWM inverter for dual-rotor wind turbine system

Benbouhenni,

Colak,

Bizon

et al. 2023

Measurement and Control

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Energy ripples are among the common problems in renewable energies as a result of using less efficient strategies. In this work, a new technique is suggested to control a doubly-fed induction generator (DFIG) using the pulse width modulation (PWM). The new technique is based on the combination of neural networks and fractional-order control to minimize the reactive and active power ripples of the DFIG-based variable speed dual-rotor wind turbine system. The suggested fractional-order neural control (FONC) with the PWM is a simple, robust and a high-performance strategy. Simulation is performed using Matlab software to validate the effectiveness of the designed control of 1.5 MW DFIG and the obtained results are compared with the traditional direct power control (DPC) in different working conditions. In addition, the comparison between the suggested control and the DPC is performed in the cases of changing or not changing the device parameters in terms of ripple ratio, dynamic response, steady-state error, current quality, and overshoot of active and reactive power of the DFIG. As compared to the DPC, the proposed FONC technique improves the active and reactive power ripples by 65.71% and 84.74%, respectively. Also, improves the overshoot of the active and reactive power by 71.33% and 91.72%, respectively. The simulation results demonstrate the high performance and robustness of the FONC technique for the parametric variations of the DFIG-based variable speed dual-rotor wind turbine system compared to the DPC control.

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Proposed particle swarm optimization technique for the wind turbine control system

Cited by 21 publications

References 27 publications

Optimized fuzzy fractional PI-based MPPT controllers for a variable-speed wind turbine

Optimized fuzzy fractional PI-based MPPT controllers for a variable-speed wind turbine

Stability Enhancement of Wind Energy Conversion Systems Based on Optimal Superconducting Magnetic Energy Storage Systems Using the Archimedes Optimization Algorithm

Fractional-order neural control of a DFIG supplied by a two-level PWM inverter for dual-rotor wind turbine system

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