Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.650
JCR 5-Year IF: 0.639
Issues per year: 4
Current issue: Aug 2019
Next issue: Nov 2019
Avg review time: 72 days


PUBLISHER

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


TRAFFIC STATS

2,358,167 unique visits
611,321 downloads
Since November 1, 2009



Robots online now
SemrushBot
SemanticScholar


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 19 (2019)
 
     »   Issue 3 / 2019
 
     »   Issue 2 / 2019
 
     »   Issue 1 / 2019
 
 
 Volume 18 (2018)
 
     »   Issue 4 / 2018
 
     »   Issue 3 / 2018
 
     »   Issue 2 / 2018
 
     »   Issue 1 / 2018
 
 
 Volume 17 (2017)
 
     »   Issue 4 / 2017
 
     »   Issue 3 / 2017
 
     »   Issue 2 / 2017
 
     »   Issue 1 / 2017
 
 
 Volume 16 (2016)
 
     »   Issue 4 / 2016
 
     »   Issue 3 / 2016
 
     »   Issue 2 / 2016
 
     »   Issue 1 / 2016
 
 
  View all issues  








LATEST NEWS

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
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

Read More »


    
 

  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 (765 KB) | Citation | Downloads: 183 | Views: 162

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
Quick view
Full text preview
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 24] [SCOPUS Times Cited 42]


[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 15] [SCOPUS Times Cited 16]


[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 72] [SCOPUS Times Cited 91]


[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 71] [SCOPUS Times Cited 82]


[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 28] [SCOPUS Times Cited 30]


[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 47] [SCOPUS Times Cited 63]


[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 4] [SCOPUS Times Cited 9]


[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 1] [SCOPUS Times Cited 2]


[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 79] [SCOPUS Times Cited 110]


[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 5] [SCOPUS Times Cited 6]


[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 11] [SCOPUS Times Cited 15]


[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 31] [SCOPUS Times Cited 39]


[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 39] [SCOPUS Times Cited 51]


[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 11] [SCOPUS Times Cited 15]


[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 6]


[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 108] [SCOPUS Times Cited 134]


[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 141] [SCOPUS Times Cited 174]


[20] S. Mirjalili, "The Ant Lion Optimizer", Advances in Engineering Software, vol. 83, pp. 80-98, 2015.
[CrossRef] [Web of Science Times Cited 438] [SCOPUS Times Cited 570]


[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 1362] [SCOPUS Times Cited 1901]


[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 354] [SCOPUS Times Cited 506]


[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 381] [SCOPUS Times Cited 256]


[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 610] [SCOPUS Times Cited 872]


[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 260] [SCOPUS Times Cited 408]


[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 177] [SCOPUS Times Cited 290]


[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] [SCOPUS Times Cited 355]


[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 4] [SCOPUS Times Cited 6]




References Weight

Web of Science® Citations for all references: 4,316 TCR
SCOPUS® Citations for all references: 6,049 TCR

Web of Science® Average Citations per reference: 139 ACR
SCOPUS® Average Citations per reference: 195 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 2019-10-13 00:50 in 190 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
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.

Copyright ©2001-2019
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: