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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Improved Wind Speed Prediction Using Empirical Mode Decomposition, ZHANG, Y., ZHANG, C., SUN, J., GUO, J.
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  3/2019 - 4

A Novel Power Curve Modeling Framework for Wind Turbines

YESILBUDAK, M. See more information about YESILBUDAK, M. on SCOPUS See more information about YESILBUDAK, M. on IEEExplore See more information about YESILBUDAK, M. on Web of Science
 
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Download PDF pdficon (765 KB) | Citation | Downloads: 484 | Views: 839

Author keywords
optimization methods, parameter estimation, partitioning algorithms, power engineering computing, wind energy generation

References keywords
wind(22), power(20), energy(17), curve(13), turbine(11), renewable(7), algorithm(6), systems(5), optimization(5), modeling(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-08-31
Volume 19, Issue 3, Year 2019, On page(s): 29 - 40
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.03004
Web of Science Accession Number: 000486574100004
SCOPUS ID: 85072171926

Abstract
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This paper presents two main novelties concerning power curve modeling of wind turbines. First novelty lies in the hybridization of 5 widely-used parametric functions and 8 recently-developed metaheuristic optimization algorithms. While constructing new hybrid power curve models, design coefficients of 4-parameter and 5-parameter logistic, 5th-order and 6th-order polynomial and modified hyperbolic tangent functions are fitted with ant lion, grey wolf, moth-flame and multi-verse optimizers and whale optimization, sine cosine, salp swarm and dragonfly algorithms. The best hybrid power curve model is achieved by the grey wolf optimizer-based modified hyperbolic tangent function in terms of the goodness-of-fit indicators. Second novelty lies in the integration of a well-known partitional clustering method to the best hybrid power curve model developed. While building a novel integrative power curve model, design coefficients of grey wolf optimizer-based modified hyperbolic tangent function are solved using only the highly representative data points identified by the Squared Euclidean-based k-means clustering algorithm. The operational characteristics of the wind turbine power curve are reflected with a higher accuracy. As a crucial result, the proposed power curve modeling framework is shown to be superior for wind turbines.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 50]


[2] M. Lydia, S. S. Kumar, A. I. Selvakumar, G. E. P. Kumar, "Wind Resource Estimation Using Wind Speed and Power Curve Models", Renewable Energy, vol. 83, pp. 425-434, 2015.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 26]


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[CrossRef] [Web of Science Times Cited 87] [SCOPUS Times Cited 102]


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[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 39]


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[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 74]


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[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 39]


[13] B. K. Saxena, K. V. S. Rao, "Comparison of Weibull Parameters Computation Methods and Analytical Estimation of Wind Turbine Capacity Factor Using Polynomial Power Curve Model: Case Study of a Wind Farm", Renewables: Wind, Water, and Solar, vol. 2, no. 3, pp. 1-11, 2015.
[CrossRef]


[14] E. Taslimi-Renani, M. Modiri-Delshad, M. F. M. Elias, N. A. Rahim, "Development of an Enhanced Parametric Model for Wind Turbine Power Curve", Applied Energy, vol. 177, pp. 544-552, 2016.
[CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 64]


[15] F. Pelletier, C. Masson, A. Tahan, "Wind Turbine Power Curve Modelling Using Artificial Neural Network", Renewable Energy, vol. 89, pp. 207-214, 2016.
[CrossRef] [Web of Science Times Cited 74] [SCOPUS Times Cited 85]


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[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 16]


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[CrossRef] [SCOPUS Times Cited 6]


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[CrossRef] [Web of Science Times Cited 133] [SCOPUS Times Cited 168]


[19] M. Lydia, S. S. Kumar, A. I. Selvakumar, G. E. P. Kumar, "Comprehensive Review on Wind Turbine Power Curve Modeling Techniques", Renewable and Sustainable Energy Reviews, vol. 30, pp. 452-460, 2014.
[CrossRef] [Web of Science Times Cited 199] [SCOPUS Times Cited 248]


[20] S. Mirjalili, "The Ant Lion Optimizer", Advances in Engineering Software, vol. 83, pp. 80-98, 2015.
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[CrossRef] [Web of Science Times Cited 2694] [SCOPUS Times Cited 3553]


[22] S. Mirjalili, "Moth-Flame Optimization Algorithm: A Novel Nature-Inspired Heuristic Paradigm", Knowledge-Based Systems, vol. 89, pp. 228-249, 2015.
[CrossRef] [Web of Science Times Cited 820] [SCOPUS Times Cited 1028]


[23] S. Mirjalili, S. M. Mirjalili, A. Hatamlou, "Multi-Verse Optimizer: A Nature-Inspired Algorithm for Global Optimization", Neural Computing and Applications, vol. 27, no. 2, pp. 495-513, Feb. 2016.
[CrossRef] [Web of Science Times Cited 886] [SCOPUS Times Cited 535]


[24] S. Mirjalili, A. Lewis, "The Whale Optimization Algorithm", Advances in Engineering Software, vol. 95, pp. 51-67, 2016.
[CrossRef] [Web of Science Times Cited 1554] [SCOPUS Times Cited 2034]


[25] S. Mirjalili, "SCA: A Sine Cosine Algorithm for Solving Optimization Problems", Knowledge-Based Systems, vol. 96, pp. 120-133, 2016.
[CrossRef] [Web of Science Times Cited 683] [SCOPUS Times Cited 926]


[26] S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, S. M. Mirjalili, "Salp Swarm Algorithm: A Bio-Inspired Optimizer for Engineering Design Problems", Advances in Engineering Software, vol. 114, pp. 163-191, 2017.
[CrossRef] [Web of Science Times Cited 624] [SCOPUS Times Cited 827]


[27] S. Mirjalili, "Dragonfly Algorithm: A New Meta-Heuristic Optimization Technique for Solving Single-Objective, Discrete, and Multi-Objective Problems", Neural Computing and Applications, vol. 27, no. 4, pp. 1053-1073, 2016.
[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 726]


[28] C. C. Aggarwal, C. K. Reddy, "Data Clustering: Algorithms and Applications", pp. 89-93, CRC Press, 2014.

[29] Open Platform for French Public Data & ENGIE, [Online] Available: Temporary on-line reference link removed - see the PDF document

[30] M. Yesilbudak, "Implementation of Novel Hybrid Approaches for Power Curve Modeling of Wind Turbines", Energy Conversion and Management, vol. 171, pp. 156-169, 2018.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 14]




References Weight

Web of Science® Citations for all references: 9,104 TCR
SCOPUS® Citations for all references: 11,872 TCR

Web of Science® Average Citations per reference: 294 ACR
SCOPUS® Average Citations per reference: 383 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-11-27 03:53 in 184 seconds.




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