Design static VAR compensator controller using artificial neural network optimized by modify Grey Wolf Optimization

Mohamed, Al-Attar Ali; El-Gaafary, Ahmed A. M.; Mossa, Mahmoud A.; Hemeida, Ashraf Mohamed

doi:10.1109/ijcnn.2015.7280704

Cited by 25 publications

(10 citation statements)

References 28 publications

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“…After the prey is traced by wolf pack, it is surrounded by the wolf pack. The encircling behaviour can be mathematically formulated as follows [14, 27] :

bold-italicD = falsefalse| bold-italicC \cdot X_{p} false(k false) - bold-italicX false(k false) falsefalse|

bold-italicX false(k + 1 false) = X_{p} false(k false) - bold-italicA \cdot bold-italicD

where k is the current iteration, A and C are coefficient vectors, X p is the position vector of the prey and X is a grey wolf's position vector.…”

Section: Gwo Overviewmentioning

confidence: 99%

“…However, this has an exploratory effect, it has a bad effect on the convergence of the conventional GWO algorithm where it decreases the convergence rate of the algorithm. Therefore, the proposed modification in [27] is employed in this paper to enhance the exploration–exploitation balance and convergence rate of the conventional GWO as follows [27]:

bold-italica = ζ \exp false(- θ \times k false)

where

ζ

and

θ

are two control parameters which rule the convergence characteristic's behaviour of GWO algorithm over the iterations k for each point. In addition, by converts the vector a to random non‐linear vector, the exploratory feature can be maintained as well as accelerates the convergence of the algorithm.…”

Section: Improved Gwomentioning

confidence: 99%

See 1 more Smart Citation

Optimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimiser

Taha

Elattar

2018

IET Generation, Transmission & Distribution

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“…After the prey is traced by wolf pack, it is surrounded by the wolf pack. The encircling behaviour can be mathematically formulated as follows [14, 27] :

bold-italicD = falsefalse| bold-italicC \cdot X_{p} false(k false) - bold-italicX false(k false) falsefalse|

bold-italicX false(k + 1 false) = X_{p} false(k false) - bold-italicA \cdot bold-italicD

where k is the current iteration, A and C are coefficient vectors, X p is the position vector of the prey and X is a grey wolf's position vector.…”

Section: Gwo Overviewmentioning

confidence: 99%

bold-italica = ζ \exp false(- θ \times k false)

where

ζ

and

θ

Section: Improved Gwomentioning

confidence: 99%

Optimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimiser

Taha

Elattar

2018

IET Generation, Transmission & Distribution

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“…Thanks to these research activities, the latest generations of wind turbines operate at variable speed to extract the maximum electrical power depending on the wind speed [1][2][3][4][5][6][7][8]. The development of power electronics control techniques has introduced intelligent controls [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Control of a variable speed asynchronous wind turbine dedicated to isolated site

Ouchbel¹,

Zouggar²,

Elhafyani³

et al. 2020

IJEECS

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<p>This paper focuses on the study of the asynchronous generator self-excited during operation in isolated mode. It concerns the analysis of a robust control of the asynchronous machine in order to improve the quality of the electrical energy produced in different environmental circumstances, and to promote the use of renewable energies in rural areas to improve education, the supply of drinking water, livestock and agriculture, access to information and communication. The present work concerns the description and modeling of the various mechanical parts of the wind turbine. It also tackles the steady-state and transient modeling of the asynchronous generator under self-excitation conditions. The practical results and the simulation ones have shown the influence of the self-excitation capacity on the output quantities of the wind system (voltage, current and torque) in vacuum and under charge (resistive and inductive). In the case where the asynchronous wind turbine is connected to a network, it imposes amplitude, waveform and frequency. But in the case of isolated sites, the asynchronous machine has a low power factor, what means it requires reactive energy. To correct this irregularity, we can improve the power factor by using variable capacitors. The excitation current (reactive power) must be permanently supplied according to the load connected. This requires an intelligent system that regulates the electrical energy produced. </p>

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“…GWO mimics the social hierarchy and hunting behavior of grey wolves in nature. Due to its excellent search capacity, it has been successfully applied to many real-world problems since its introduction, like optimal reactive power dispatch problem [ 17 ], parameter estimation in surface waves [ 18 ], static VAR compensator controller design [ 19 ], blackout risk prevention in a smart grid [ 20 ], capacitated vehicle routing problem [ 21 ], nonconvex economic load dispatch problem [ 22 ], and so on. However, it should be noted that the initial population of original GWO is generated in a random way.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

Chen

Huang

et al. 2017

Computational and Mathematical Methods in Medicine

217

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In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.

show abstract

Design static VAR compensator controller using artificial neural network optimized by modify Grey Wolf Optimization

Cited by 25 publications

References 28 publications

Optimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimiser

Optimal reactive power resources sizing for power system operations enhancement based on improved grey wolf optimiser

Control of a variable speed asynchronous wind turbine dedicated to isolated site

An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

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