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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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  3/2019 - 4

 HIGHLY CITED PAPER 

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: 936 | Views: 1,709

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

[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 42] [SCOPUS Times Cited 65]


[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 29] [SCOPUS Times Cited 36]


[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 143] [SCOPUS Times Cited 169]


[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 126] [SCOPUS Times Cited 147]


[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 58] [SCOPUS Times Cited 64]


[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 82] [SCOPUS Times Cited 104]


[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 49] [SCOPUS Times Cited 63]


[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 Times Cited 31] [SCOPUS Times Cited 39]


[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 147] [SCOPUS Times Cited 192]


[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 35] [SCOPUS Times Cited 39]


[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 63] [SCOPUS Times Cited 77]


[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 71] [SCOPUS Times Cited 90]


[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 121] [SCOPUS Times Cited 144]


[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 16] [SCOPUS Times Cited 21]


[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] [SCOPUS Times Cited 8]


[18] C. Carrillo, A. F. Obando-Montano, J. Cidras, E. Diaz-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 205] [SCOPUS Times Cited 249]


[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 292] [SCOPUS Times Cited 359]


[20] S. Mirjalili, "The Ant Lion Optimizer", Advances in Engineering Software, vol. 83, pp. 80-98, 2015.
[CrossRef] [Web of Science Times Cited 1676] [SCOPUS Times Cited 2039]


[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 7092] [SCOPUS Times Cited 8988]


[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 2128] [SCOPUS Times Cited 2530]


[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 1845] [SCOPUS Times Cited 1476]


[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 4912] [SCOPUS Times Cited 6072]


[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 2200] [SCOPUS Times Cited 2658]


[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 2192] [SCOPUS Times Cited 2621]


[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 680] [SCOPUS Times Cited 1629]


[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 27] [SCOPUS Times Cited 32]




References Weight

Web of Science® Citations for all references: 24,262 TCR
SCOPUS® Citations for all references: 29,911 TCR

Web of Science® Average Citations per reference: 783 ACR
SCOPUS® Average Citations per reference: 965 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 2023-03-13 20:46 in 156 seconds.




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