Skip to main content

Advertisement

SpringerLink
Log in

RETRACTED ARTICLE: Adaptive neuro-fuzzy evaluation of wind farm power production as function of wind speed and direction

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

This article was retracted on 29 May 2019

This article has been updated

Abstract

Wind velocity assumes a critical part for measuring the power created by the wind turbines. Nonetheless, power production from wind has a few weaknesses. One significant issue is that wind is a discontinuous energy source which implies that there exists substantial variability in the generation of vigor because of different variables, for example, wind speed. Wind direction is a significant variable for proficient turbine control for getting the most energy with a given wind speed. Taking into account the conjectures on wind heading, it might be conceivable to adjust the turbine to the wind bearing to get the most energy yield. Since both forecasts of wind speed and direction are basic for effective wind energy collecting it is crucial to develop a methodology for estimation of wind speed and direction and afterwards to estimate wind farm power production as function of wind pace and heading distribution. Despite the fact that various numerical functions have been proposed for demonstrating the wind speed and direction frequency distribution, there are still disadvantages of the models like very demanding in terms of calculation time. In this investigation adaptive neuro-fuzzy inference system (ANFIS), which is a particular sort of the artificial neural networks (ANN) family, was used to anticipate the wind speed and direction frequency dispersion. Thereafter, the ANFIS system was utilized to gauge wind homestead power creation as function of wind velocity and bearing. Neural system in ANFIS modifies parameters of enrollment capacity in the fuzzy logic of the fuzzy inference system. The reenactment outcomes exhibited in this paper demonstrate the adequacy of the created technique.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Change history

  • 29 May 2019

    The Editor-in-Chief has retracted this article (Petković et al. 2015a) because validity of the content of this article cannot be verified.

  • 29 May 2019

    The Editor-in-Chief has retracted this article (Petkovi�� et al. 2015a) because validity of the content of this article cannot be verified.

References

  • Adankon MM, Cheriet M (2011) Help-training for semi-supervised support vector machines. Pattern Recognit 44:2220–2230

    Article  Google Scholar 

  • Ahmed E, Shiraz M, Gani A (2013) Spectrum-aware distributed channel assignment in cognitive radio wireless mesh networks. Malays J Comput Sci 26(3):232–250

    Google Scholar 

  • Al-Ghandoor A, Samhouri M (2009) Electricity consumption in the industrial sector of Jordan: application of multivariate linear regression and adaptive neuro-fuzzy techniques. Jordan J Mech Ind Eng 3(1):69–76

    Google Scholar 

  • Anuar NB, Sallehudin H, Gani A, Zakari O (2008) Identifying false alarm for network intrusion detection system using hybrid data mining and decision tree. Malays J Comput Sci 21(2):101–115

    Article  Google Scholar 

  • Ata R, Kocyigit Y, Kocyigit Y (2010) An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines. Expert Syst Appl 37:5454–5460

    Article  Google Scholar 

  • Bensoussan A, Bertrand PR, Brouste A (2012) Forecasting the energy produced by a windmill on a yearly basis. Stoch Environ Res Risk Assess 26:1109–1122. doi:10.1007/s00477-012-0565-1

    Article  Google Scholar 

  • Bottcher F, Barth S, Peinke J (2007) Small and large scale fluctuations in atmospheric wind speeds. Stoch Environ Res Risk Assess 21:299–308. doi:10.1007/s00477-006-0065-2

    Article  Google Scholar 

  • Carta JA, Velázquez S (2011) A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site. Energy 36:2671–2685

    Article  Google Scholar 

  • Carta JA, Ramirez P, Bueno C (2008) A joint probability density function of wind speed and direction for wind energy analysis. Energy Convers Manag 49:1309–1320

    Article  Google Scholar 

  • Carta JA, Ramırez P, Velazquez S (2009) A review of wind speed probability distributions used in wind energy analysis: case studies in the Canary Islands. Renew Sustain Energy Rev 13:933–955

    Article  Google Scholar 

  • Chellalin F, Khellaf A, Belouchrani A (2013) Identification and analysis of wind speed patterns extracted from multi-sensors measurements. Stoch Environ Res Risk Assess 27:1–9. doi:10.1007/s00477-012-0558-0

    Article  Google Scholar 

  • Ekici BB, Aksoy UT (2011) Prediction of building energy needs in early stage of design by using ANFIS. Expert Syst Appl 38:5352

    Article  Google Scholar 

  • Enayatifar R, Sadaei HJ, Abdullah AH, Gani A (2013) Imperialist competitive algorithm combined with refined high-order weighted fuzzy time series for short term load forecasting. Energy Convers Manag 76:1104–1116

    Article  Google Scholar 

  • Erdem E, Shi J (2011) ARMA based approaches for forecasting the tuple of wind speed and direction. Appl Energy 88:1405–1414

    Article  Google Scholar 

  • Gallego C, Pinson P, Madsen H, Costa A, Cuerva A (2011) Influence of local wind speed and direction on wind power dynamics—application to offshore very short-term forecasting. Appl Energy 88:4087–4096

    Article  Google Scholar 

  • Inal M (2008) Determination of dielectric properties of insulator materials by means of ANFIS: a comparative study. Expert Syst Appl 195:34

    CAS  Google Scholar 

  • Jain P, Garibaldib JM, Hirst JD (2009) Supervised machine learning algorithms for protein structure classification. Comput Biol Chem 33:216–223

    Article  CAS  Google Scholar 

  • Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  • Khajeh A, Modarress H, Rezaee B (2009) Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers. Expert Syst Appl 36:5728

    Article  Google Scholar 

  • Kurnaz S, Cetin O, Kaynak O (2010) Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles. Expert Syst Appl 37:1229–1234

    Article  Google Scholar 

  • Lo SP, Lin YY (2005) The prediction of wafer surface non-uniformity using FEM and ANFIS in the chemical mechanical polishing process. J Mater Process Technol 168:250

    Article  CAS  Google Scholar 

  • Lopez P, Velo R, Maseda F (2008) Effect of direction on wind speed estimation in complex terrain using neural networks. Renew Energy 33:2266–2272

    Article  Google Scholar 

  • Mansoori M, Zakaria O, Gani A (2012) Improving exposure of intrusion deception system through implementation of hybrid honeypot. Int Arab J Inf Technol 9(5):436–444

    Google Scholar 

  • Mohandes M, Rehman S, Rahman SM (2011) Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Appl Energy. doi:10.1016/j.apenergy.2011.04.015

    Article  Google Scholar 

  • Ornella L, Tapia E (2010) Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data. Comput Electron Agric 74:250–257

    Article  Google Scholar 

  • Petković D, Ćojbašić Ž (2012) Adaptive neuro-fuzzy estimation of automatic nervous system parameters effect on heart rate variability. Neural Comput Appl 21(8):2065–2070

    Article  Google Scholar 

  • Petković D, Issa M, Pavlović ND, Pavlović NT, Zentner L (2012a) Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties. Expert Syst Appl 39:9477–9482. ISSN 0957-4174

    Article  Google Scholar 

  • Petković D, Issa M, Pavlović ND, Zentner L, Ćojbašić Ž (2012b) Adaptive neuro fuzzy controller for adaptive compliant robotic gripper. Expert Syst Appl 39:13295–13304. ISSN 0957-4174

    Article  Google Scholar 

  • Rajasekaran S, Gayathri S, Lee T-L (2008) Support vector regression methodology for storm surge predictions. Ocean Eng 35:1578–1587

    Article  Google Scholar 

  • Sedighizadeh M, Rezazadeh A (2008) Adaptive PID control of wind energy conversion systems using RASP1 mother wavelet basis function networks. In: Proceedings of world academy of science, engineering and technology, vol 27. ISSN 1307-6884

  • Shamshirband S, Anuar NB, Kiah MLM, Patel A (2013) An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique. Eng Appl Artif Intell 26(9):2105–2127

    Article  Google Scholar 

  • Shiraz M, Gani A, Khokhar RH, Buyya R (2012) A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing. IEEE Commun Surv Tutor 15(3):1294–1313. doi:10.1109/SURV.2012.111412.00045

    Article  Google Scholar 

  • Singh R, Kianthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12:40–45

    Article  Google Scholar 

  • Tian L, Collins C (2005) Adaptive neuro-fuzzy control of a flexible manipulator. Mechatronics 15:1305–1320

    Article  Google Scholar 

  • Xie K, Wang K (2011) Measurement of wind speed and direction with ultrasonic sensor using FPGA. Energy Procedia 12:837–843

    Article  Google Scholar 

  • Yang H, Huang K, King I, Lyu MR (2009) Localized support vector regression for time series prediction. Neurocomputing 72:2659–2669

    Article  Google Scholar 

Download references

Acknowledgments

This paper is supported by the Malaysian Ministry of Education under the University of Malaya High Impact Research Grant UM.C/625/1/HIR/MoE/FCSIT/12. This paper is also supported by Project Grant ТР35005 "Research and development of new generation wind turbines of high-energy efficiency" (2011-2014) financed by Ministry of Education, Science and Technological Development, Republic of Serbia.

Author information

Authors and Affiliations

  1. Deparment for Mechatronics and Control, Faculty of Mechanical Engineering, University of Niš, Aleksandra Medvedeva 14, 18000, Nis, Serbia

    Dalibor Petković

  2. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shahaboddin Shamshirband, Nor Badrul Anuar, Miss Laiha Mat Kiah & Abdullah Gani

  3. Department of Civil Engineering, Faculty of Engineering, 50603, Kuala Lumpur, Malaysia

    Sareh Naji

  4. Department of Computer Science, Islamic Azad University, Chalous Branch, 46615-397, Chalous , Iran

    Shahaboddin Shamshirband

Authors
  1. Dalibor Petković
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shahaboddin Shamshirband
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nor Badrul Anuar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sareh Naji
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Miss Laiha Mat Kiah
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Abdullah Gani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dalibor Petković.

Additional information

The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with the articles cited [2-8]), peer review and authorship manipulation. The authors do not agree to this retraction.

References

1. Petković, D., Shamshirband, S., Anuar, N.B. et al. Stoch Environ Res Risk Assess (2015) 29: 793. https://doi.org/10.1007/s00477-014-0901-8

2. Basser, H., Shamshirband, S., Petković, D. et al. Nat Hazards (2014) 73: 1439. https://doi.org/10.1007/s11069-014-1145-0

3. Petković D., Ćojbašić Ž., Nikolić V. et al. Energy (2015) 64: 868-874. https://doi.org/10.1016/j.energy.2013.10.094

4. Petković D., Shamshirband, S., Iqbal J. et al. Applied Soft Computing (2015) 22: 424-431. https://doi.org/10.1016/j.asoc.2014.04.037

5. Petković D., Shamshirband S., Anuar N.B. et al. Energy Conversion and Management (2014) 84: 133-139. https://doi.org/10.1016/j.enconman.2014.04.010

6. Petković, D., Ab Hamid, S.H., Ćojbašić, Ž. et al. Nat Hazards (2014) 74: 463. https://doi.org/10.1007/s11069-014-1189-1

7. Erdem E., Shi J. Applied Energy (2011) 88(4): 1405-1414. https://doi.org/10.1016/j.apenergy.2010.10.031

8. Basser, H., Shamshirband, S., Karami, H. et al. Nat Hazards (2014) 73: 1393. https://doi.org/10.1007/s11069-014-1140-5

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Petković, D., Shamshirband, S., Anuar, N.B. et al. RETRACTED ARTICLE: Adaptive neuro-fuzzy evaluation of wind farm power production as function of wind speed and direction. Stoch Environ Res Risk Assess 29, 793–802 (2015). https://doi.org/10.1007/s00477-014-0901-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-014-0901-8

Keywords

Search

Navigation