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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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2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

2018-May-31
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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
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (759 KB) | Citation | Downloads: 103 | Views: 95

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

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

[1] E. Sainz, A. Llombart, J. J. Guerrero, "Robust Filtering for the Characterization of Wind Turbines: Improving Its Operation and Maintenance", Energy Conversion and Management, vol. 50, no. 9, pp. 2136-2147, 2009.
[CrossRef] [Web of Science Times Cited 23]


[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 14]


[3] A. Marvuglia, A. Messineo, "Monitoring of Wind Farms’ Power Curves Using Machine Learning Techniques", Applied Energy, vol. 98, pp. 574-583, 2012.
[CrossRef] [Web of Science Times Cited 71]


[4] L. C. Pagnini, M. Burlando, M. P. Repetto, "Experimental Power Curve of Small-Size Wind Turbines in Turbulent Urban Environment", Applied Energy, vol. 154, pp. 112-121, 2015.
[CrossRef] [Web of Science Times Cited 70]


[5] H. Long, L. Wang, Z. Zhang, Z. Song, J. Xu, "Data-Driven Wind Turbine Power Generation Performance Monitoring", IEEE Transactions on Industrial Electronics, vol. 62, no. 10, pp. 6627-6635, 2015.
[CrossRef] [Web of Science Times Cited 27]


[6] T. P. Chang, F. J. Liu, H. H. Ko, S. P. Cheng, S. C. Kuo, "Comparative Analysis on Power Curve Models of Wind Turbine Generator in Estimating Capacity Factor", Energy, vol. 73, pp. 88-95, 2014.
[CrossRef] [Web of Science Times Cited 46]


[7] J. Yan, T. Ouyang, "Advanced Wind Power Prediction Based on Data-Driven Error Correction", Energy Conversion and Management, vol. 180, pp. 302-311, Jan. 2019.
[CrossRef] [Web of Science Times Cited 3]


[8] S. Seo, S. D. Oh, H. Y. Kwak, "Wind Turbine Power Curve Modeling Using Maximum Likelihood Estimation Method", Renewable Energy, vol. 136, pp. 1164-1169, 2019.
[CrossRef] [Web of Science Record]


[9] C. Kamalakannan, L. Padma, S. S. S. Dash, B. K. Panigrahi, "Power Electronics and Renewable Energy Systems", pp. 1407-1414, Springer, 2015.

[10] M. Lydia, A. I. Selvakumar, S. S. Kumar, G. E. P. Kumar, "Advanced Algorithms for Wind Turbine Power Curve Modeling", IEEE Transactions on Sustainable Energy, vol. 4, no. 3, pp. 827-835, 2013.
[CrossRef] [Web of Science Times Cited 78]


[11] D. Villanueva, A. Feijoo, "Comparison of Logistic Functions for Modeling Wind Turbine Power Curves", Electric Power Systems Research, vol. 155, pp. 281-288, 2018.
[CrossRef] [Web of Science Times Cited 4]


[12] M. Marciukaitis, I. Zutautaite, L. Martisauskas, B. Joksas, A. Sfetsos, "Non-Linear Regression Model for Wind Turbine Power Curve", Renewable Energy, vol. 113, pp. 732-741, 2017.
[CrossRef] [Web of Science Times Cited 10]


[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 30]


[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 36]


[16] X. Liu, "An Improved Interpolation Method for Wind Power Curves", IEEE Transactions on Sustainable Energy, vol. 3, no. 3, pp. 528-534, 2012.
[CrossRef] [Web of Science Times Cited 10]


[17] A. K. Das, "An Empirical Model of Power Curve of a Wind Turbine", Energy Systems, vol. 5, no. 3, pp. 507-518, 2014.
[CrossRef]


[18] C. Carrillo, A. F. Obando-Montano, J. Cidrás, E. Díaz-Dorado, "Review of Power Curve Modelling for Wind Turbines", Renewable and Sustainable Energy Reviews, vol. 21, pp. 572-581, 2013.
[CrossRef] [Web of Science Times Cited 105]


[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 139]


[20] S. Mirjalili, "The Ant Lion Optimizer", Advances in Engineering Software, vol. 83, pp. 80-98, 2015.
[CrossRef] [Web of Science Times Cited 424]


[21] S. Mirjalili, S. M. Mirjalili, A. Lewis, "Grey Wolf Optimizer", Advances in Engineering Software, vol. 69, pp. 46-61, 2014.
[CrossRef] [Web of Science Times Cited 1311]


[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 341]


[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 362]


[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 572]


[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 249]


[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 165]


[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 43]


[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 2]




References Weight

Web of Science® Citations for all references: 4,135 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 133 ACR
SCOPUS® Average Citations per reference: 0

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 2019-09-17 03:38 in 189 seconds.




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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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