Journal of Green Energy Research and Innovation
Quarterly

The Impact of Wind Direction on Wind Farms’ Output Power and Income

Document Type : Research article

Authors

Ali Asghar Karimi Taleb 1
Hojatollah Makvandi 2
Ashknaz Oraee 3

1 Khuzestan Water and Power Authority, Ahvaz, 613481395, Iran.

2 Abadan Oil Refining Company, Abadan, Iran.

3 Faculty of Electrical and Medical Engineering, Sadjad University of Technology, Mashhad, Iran.

https://doi.org/10.61186/jgeri.1.1.34
Abstract
The main methodology in every wind power prediction model involves converting wind speed into power using the power output curve of the wind turbine. However, preceding studies that have introduced models for such curves have not considered the impact of wind direction and its recurring fluctuations over time on predicting wind turbine power output. The main focus of these studies has just been on the magnitude of wind speed and the relationship between wind speed and turbine power. The present study models the effect of wind direction on wind turbine power output and uses it to modify the quadratic power curve equations. Using these modified equations and considering the turbine mechanism to follow the wind direction, a method is presented for predicting wind turbine power output under frequent changes in wind direction over time. To deal with the lack of access to long term and high-resolution wind data, registered historical data and probabilistic distribution functions are used to produce lost data with software. To demonstrate the efficacy of the suggested approach, the real data recorded for a 1.5 MW turbine installed in Khaf in Razavi Khorasan, Iran, are used as a case study. Finally, the potential wind power and potential income of the four windy regions in Iran were assessed based on the payment mechanism of the Organization of Renewable Energy and Electricity Efficiency of Iran, assuming the same installed capacity. The effect of wind direction and its variations over time, which can affect power output of wind turbine and income, is the main focus of this section of paper.

Graphical Abstract

The Impact of Wind Direction on Wind Farms’ Output Power and Income

Highlights

 

  • Modifying The quadratic power curve equation
  • The derivation of new parameters for predicting the output power of a WT
  • Wind direction and its variation for predicting power and income of a WT
  • Proposing a new approach to study the wind power potential of a region

Keywords

Wind energy
Wind speed prediction
Wind turbine
Wind farm income

 

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The ethical issues, including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, have been completely observed by the authors.

 

Credit Authorship Contribution Statement

AliAsghar Karimi Taleb: Conceptualization, Formal analysis, Project administration, Supervision, Validation, Roles/Writing - original draft. Hojatollah Makvandi: Conceptualization, Investigation, Methodology, Resources, Visualization, Writing - review & editing. Ashknaz Oraee: Formal analysis, Investigation, Software, Roles/Writing - original draft.

 

Bibliography

 AliAsghar Karimi Taleb received the B.Eng. degree in Electronic Engineering from Razi University Kermanshah, Kermanshah, Iran., in 2014, and the MSc. degree in Power System Engineering from Sharif University of Technology, Tehran, Iran, in 2017. He is a PhD candidate in Electrical Power Engineering in Shahid Chamran University of Ahvaz, Ahvaz, Iran. He works as a design and supervision expert for the installation of electrical facilities and structures, Khuzestan Water and Power Authority (KWPA), Ministry of Energy of Iran, Ahvaz, Iran. His current research interests include smart grid and microgrid and influence of renewable energy on power systems.

Hojatollah Makvandi was born in 1975 in Iran. He received the Ph.D. degree in Electrical Power Engineering in 2020 from the Dezful Branch, Islamic Azad University, Dezful, Iran. He is a utility and power plant manager in the Abadan Oil Refining Company. His research interests include gas and steam turbine, micro-grid and islanding, and FACTS devices.

Ashknaz Oraee (Member, IEEE) received the B.Eng. degree in Electrical Engineering from Kings College London, London, U.K., in 2011, and the Ph.D. degree in Electrical Engineering from Cambridge University, Cambridge, U.K., in 2015, focusing on electrical machine design and optimization. She is a member of faculty in Sadjad University, Mashhad, Iran. Her current research interests include electrical machines and drives for renewable power generation.

 

Citation

A. A. Karimi Taleb, H. Makvandi, and A. Oraee " The Impact of Wind Direction on Wind Farms’ Output Power ‎and Income‎," Journal of Green Energy Research and Innovation, vol. 1, no. 1, pp. 34-47, 2024.

 
[1] F. Bilendo, H. Badihi, N. Lu, P. Cambron, and B. Jiang, "An Intelligent Data-Driven Machine Learning Approach for Fault Detection of Wind Turbines," in 2021 6th International Conference on Power and Renewable Energy (ICPRE), pp. 444-449, 2021.
[2] M. F. Nezhadnaeini, M. Hajivand, M. Abasi, and S. Mohajeryami, "Optimal Allocation of Distributed Generation Units Based on Two Different Objectives by A Novel Version Group Search Optimizer Algorithm in Unbalanced Loads System," Rev Roumaine des Sciences Techniques-Series Electrotechnique et Energetique, vol. 61, no. 4, pp. 338–342, 2016‎‏.‏
[3] M. Abasi, M. Joorabian, A. Saffarian, and S. G. Seifossadat, "A Comprehensive Review of Various Fault Location Methods for Transmission Lines Compensated by FACTS Devices and Series Capacitors," Journal of Operation and Automation in Power Engineering, vol. 9, no. 3, pp. 213-225, 2021.
[4] X. Jin, Z. Xu, and W. Qiao, "Condition Monitoring of Wind Turbine Generators Using Scada Data Analysis," IEEE Transactions on Sustainable Energy, vol. 12, no. 1, pp. 202-210, 2020.
[5] M. Sadeghi, and M. Abasi, "Optimal Placement and Sizing of Hybrid Superconducting Fault Current Limiter for Protection Coordination Restoration of The Distribution Networks in The Presence of Simultaneous Distributed Generation," Electric Power Systems Research, vol. 201, 107541, 2021.
[6] C. Carrillo, A. F. Obando Montano, J. Cidras, and E. Diaz-Dorado, "Review of Power Curve Modelling for Wind Turbines," Renewable and Sustainable Energy Reviews, vol. 21, pp. 572-581, 2013.
[7] M. Abasi, A. T. Farsani, A. Rohani, and M. A. Shiran, "Improving Differential Relay Performance During Cross-Country Fault Using a Fuzzy Logic-Based Control Algorithm," in 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), pp. 193-199, 2019.
[8] M. Abasi, M. Falah Nezhadnaeini, M. Karimi, and N. Yousefi, "A Novel Metaheuristic Approach to Solve Unit Commitment Problem in the Presence of Wind Farms," Rev Roumaine des Sciences Techniques-Series Electrotechnique et Energetique, vol. 60, no. 3, pp. 253–262, 2015‎‏.‏
[9] M. Lydia, A. I. Selvakumar, S. S. Kumar, and 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.
[10] V. Thapar, G. Agnihotri, and V. K. Sethi, "Critical Analysis of Methods for Mathematical Modelling of Wind Turbines," Renewable Energy, vol. 36, no. 11, pp. 3166-3177, 2011.
[11] S. Gill, B. Stephen, and S. Galloway, "Wind Turbine Condition Assessment Through Power Curve Copula Modeling," IEEE Transactions on Sustainable Energy, vol. 3, no. 1, pp. 94-101, 2011.
[12] A. Marvuglia, and A. Messineo, "Monitoring of Wind Farms Power Curves Using Machine Learning Techniques," Applied Energy, vol. 98, pp. 574-583, 2012.
[13] J. Yan, H. Zhang, Y. Liu, S. Han, and L. Li, "Uncertainty Estimation for Wind Energy Conversion by Probabilistic Wind Turbine Power Curve Modelling," Applied Energy, vol. 239, pp. 1356-1370, 2019.
[14] D. Villanueva, and A. Feijoo, "A Review on Wind Turbine Deterministic Power Curve Models," Applied Sciences, vol. 10, no. 12, 4186, 2020.
[15] S. Seo, S. D. Oh, and H. Y. Kwak, "Wind Turbine Power Curve Modeling Using Maximum Likelihood Estimation Method," Renewable Energy, vol. 136, pp. 1164-1169, 2019.
[16] D. Villanueva, A. Sixto, A. Feijoo, A. Fernandez, and E. Miguez, "Methods to Apply A 3-Parameter Logistic Model to Wind Turbine Data," Applied Sciences, vol. 10, no. 9, 3317, 2020.
[17] S. M. Sadeghi, M. Daryalal, and M. Abasi, "Two‐Stage Planning of Synchronous Distributed Generations in Distribution Network Considering Protection Coordination Index and Optimal Operation Situation," IET Renewable Power Generation, vol. 16, no. 11, pp. 2338-2356, 2022.
[18] M. Abasi, A. Saffarian, M. Joorabian, and S. G. Seifossadat, "Location of Double-Circuit Grounded Cross-Country Faults in GUPFC-Compensated Transmission Lines Based on Current and Voltage Phasors Analysis," Electric Power Systems Research, vol. 195, 107124, 2021.
[19] S. Han, Y. Qiao, et al., "Wind Turbine Power Curve Modeling Based on Interval Extreme Probability Density for the Integration of Renewable Energies and Electric Vehicles," Renewable Energy, vol. 157, pp. 190-203, 2020.
[20] G. Demurtas "Power Curve Measurement with Spinner Anemometer According to IEC 61400-12-2.", DTU Wind Energy. DTU Wind Energy I No. 0440, 2015.
[21] M. Abasi, A. T. Farsani, A. Rohani, and A. Beigzadeh, "A Novel Fuzzy Theory-Based Differential Protection Scheme for Transmission Lines," International Journal of Integrated Engineering, vol. 12, no. 8, pp. 149-160, 2020.
[22] S. Markarian, F. Fazelpour, and E. Markarian, "Optimization of Wind Farm Layout Considering Wake Effect and Multiple Parameters," Environmental Progress & Sustainable Energy, vol. 38, no. 5, 13193, 2019.
[23] R. K. Pandit, D. Infield, and J. Carroll, "Incorporating Air Density into A Gaussian Process Wind Turbine Power Curve Model for Improving Fitting Accuracy," Wind Energy, vol. 22, no. 12, pp. 1675-1687, 2019.
[24] S. Diaz, J. A. Carta, and J. M. Matias, "Performance Assessment of Five MCP Models Proposed for the Estimation of Long-Term Wind Turbine Power Outputs at a Target Site Using Three Machine Learning Techniques," Applied Energy, vol. 209, pp. 455-477, 2018.
[25] P. Giorsetto, and K. F. Utsurogi, "Development of a New Procedure for Reliability Modeling of Wind Turbine Generators," IEEE Transactions on Power Apparatus and Systems, vol. 102, no. 1, pp. 134-143, 1983.
[26] M. Abasi, N. Heydarzadeh, and A. Rohani, "Broken Conductor Fault Location in Power Transmission Lines Using GMDH Function and Single-Terminal Data Independent of Line Parameters," Journal of Applied Research in Electrical Engineering, vol. 1, no. 1, pp. 22-32, 2021.
[27] Ministry of Energy, Renewable Energy and Energy Efficiency Organization (SATBA).
[28] Iran Grid Management Company.
 
Journal of Green Energy Research and Innovation
Volume 1, Issue 1
Winter 2024
Pages 34-47

  • Receive Date 26 November 2023
  • Revise Date 10 January 2024
  • Accept Date 21 January 2024
  • Publish Date 04 March 2024

Home

Guide for Authors

Submit Manuscript

Reviewers

Contact Us