Output power smoothing of grid‐connected permanent‐magnet synchronous generator driven directly by variable speed wind turbine: a review

Qais, Mohammed H.; Hasanien, Hany M.; Alghuwainem, Saad

doi:10.1049/joe.2017.0633

Cited by 13 publications

(4 citation statements)

References 46 publications

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“…Many other conventional methods, such as the stochastic method [13], the input-output diffusive approach [17], and the proper generalized decomposition approach [18] are presented. In this regard, conventional methods suffer from many disadvantages such as (1) they depend on the initial conditions of the problem; (2) they rely on the accuracy of the differential equation solvers [19]; and (3) they may be trapped in a local best solution, not a global solution [20]. It is very difficult for these traditional optimization methods to deal with nonlinear optimization problems such as PEMFC modeling.…”

Section: Related Workmentioning

confidence: 99%

Optimal PEM Fuel Cell Model Using a Novel Circle Search Algorithm

Qais¹,

Hasanien

Turky

et al. 2022

Electronics

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The aim of this article is to introduce a novel Circle Search Algorithm (CSA) with the purpose of obtaining a precise electrical model of a proton exchange membrane fuel cell (PEMFC). Current-voltage and current-power curves are used to characterize the performance of PEMFCs. A nonlinear model with seven unknown parameters is used to describe these polarization curves. Estimating these unknown parameters is a critical issue because they influence the dynamic analysis of fuel cells in a variety of applications such as transportation and smart grids. The suggested method is based on minimizing the fitness function (the sum of the squared errors (SSE)) between estimated and measured voltage values. The CSA is compared to the neural network algorithm (NNA), grey wolf optimization (GWO), and the sine cosine algorithm (SCA). The optimization results reveal that the simulation times of the CSA, NNA, GWO, and SCA are 5.2, 6, 5.8, and 5.75 s, respectively. Moreover, the CSA converges to the best minimum within the first 100 iterations, which is faster than the other algorithms. The robustness of the CSA is verified using 20 independent runs, where the CSA achieves the smallest average and standard deviation. In addition, the t-test proves the superiority of the CSA compared to the other algorithms, where all p-values are less than 5%. The simulated I-V and I-P curves of the CSA-PEMFC model match the measured curves very closely. Moreover, the efficacy of the CSA-PEMFC model is evaluated under a variety of temperature and pressure conditions. Therefore, the suggested CSA-PEMFC model has the potential to be an accurate and efficient model.

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Section: Related Workmentioning

confidence: 99%

Optimal PEM Fuel Cell Model Using a Novel Circle Search Algorithm

Qais¹,

Hasanien

Turky

et al. 2022

Electronics

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“…The vector control has utilized in controlling the generator-and grid-side converters. In this study, the vector control depends on the proportional-integral (PI) controller [14][15][16][17]. The proportionalderivative (PD) and proportional-integral-derivative (PID) controllers are not more attractive to be utilized in the industry due to the disadvantages of derivative control action, where it increase the input harmonics frequency to the system, and to prevent this problem, a designed filter should be utilized.…”

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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“…Therefore, several problems have arisen, and these issues should be studied, investigated, and solved to obtain an efficient operation of wind energy conversion systems [6]. In fact, most of these problems come from the sophisticated and intermittent nature of wind power, which is based on the cube of wind speed [7]. One of the main problems that this wind power system faces is enhancing the low voltage ride through (LVRT) of the grid-connected wind energy systems [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal Transient Search Algorithm-Based PI Controllers for Enhancing Low Voltage Ride-Through Ability of Grid-Linked PMSG-Based Wind Turbine

2020

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This paper depicts a new attempt to apply a novel transient search optimization (TSO) algorithm to optimally design the proportional-integral (PI) controllers. Optimal PI controllers are utilized in all converters of a grid-linked permanent magnet synchronous generator (PMSG) powered by a variable-speed wind turbine. The converters of such wind energy systems contain a generator-side converter (GSC) and a grid-side inverter (GSI). Both of these converters are optimally controlled by the proposed TSO-based PI controllers using a vector control scheme. The GSC is responsible for regulating the maximum power point, the reactive generator power, and the generator currents. In addition, the GSI is essentially controlled to control the point of common coupling (PCC) voltage, DC link voltage, and the grid currents. The TSO is applied to minimize the fitness function, which has the sum of these variables' squared error. The optimization problem's constraints include the range of the proportional and integral gains of the PI controllers. All the simulation studies, including the TSO code, are implemented using PSCAD software. This represents a salient and new contribution of this study, where the TSO is coded using Fortran language within PSCAD software. The TSO-PI control scheme's effectiveness is compared with that achieved by using a recent grey wolf optimization (GWO) algorithm–PI control scheme. The validity of the proposed TSO–PI controllers is tested under several network disturbances, such as subjecting the system to balanced and unbalanced faults. With the optimal TSO–PI controller, the low voltage ride-through ability of the grid-linked PMSG can be further improved.

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Output power smoothing of grid‐connected permanent‐magnet synchronous generator driven directly by variable speed wind turbine: a review

Cited by 13 publications

References 46 publications

Optimal PEM Fuel Cell Model Using a Novel Circle Search Algorithm

Optimal PEM Fuel Cell Model Using a Novel Circle Search Algorithm

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

Optimal Transient Search Algorithm-Based PI Controllers for Enhancing Low Voltage Ride-Through Ability of Grid-Linked PMSG-Based Wind Turbine

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