[{“box”:0,”content”:”[if 992 equals=”Open Access”]n
n
Open Access
nn
n
n[/if 992]n
n
n
n
n
n
M. Kalyani, K. Jossia Joseph, C. Anoopa Prasad, K. Thirumurugan, R. Sundar, M. Arul Muthiah
n
- n t
n
n
n[/foreach]
n
n[if 2099 not_equal=”Yes”]n
- [foreach 286] [if 1175 not_equal=””]n t
- Scientist-E, Scientist-E, Project Scientist-I, Scientist-F, Scientist-F, Scientist-F Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai, Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai, Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai, Tamil Nadu, India, Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai, Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai, Ocean Observation Systems Group, National Institute of Ocean Technology, Chennai Tamil Nadu, Tamil Nadu, Tamil Nadu, Tamil Nadu, Tamil Nadu, Tamil Nadu India, India, India, India, India, India
n[/if 1175][/foreach]
n[/if 2099][if 2099 equals=”Yes”][/if 2099]n
Abstract
nSeasonal variation of wind speed (U10) and its distribution are essential for the design of wind energy converters (WEC). In this study, year-round wind speed observations from a deepwater moored buoy at a location in the Bay of Bengal are used to assess the potential for wind power generation and are used to validate the ERA5 model. Model-U10 distribution is confined to low wind speeds with high occurrence in the medium range while observations spread to higher range. Model under-predicted monthly averaged peak winds (9.5–10 m/s) by 1.5 m/s during Southwest monsoon (June and July). Model power is always underpredicted. The peak power density during both SW (July, 667 W/m2) and NE monsoons (November, 405 W/m2) is under-predicted by 36%. The model performed well during the calm period (March) and the minimum deviation during pre (April) and post (September) monsoon are 12% and 16% respectively. The maximum deviations observed during NE (October) and SW (June) monsoon are 60% and 42% respectively. This study reveals that observations are essential to validate the model. The power density is fitted and expressed as a second order polynomial of U10. The U10 variations are expressed in terms of Weibull shape and scale parameters, which are calculated by graphical Least Squares Fit method. The monthly variation of Weibull scale parameter closely matches with average wind speed. Based on the shape parameter value, the nature of the wind and its stability during different seasons are classified for buoy-U10 and model-U10 and the deviations in the model are detailed.
n
Keywords: Wind speed, observations, ERA5, Weibull parameters, wind power density
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Alternate Energy Sources & Technologies(joaest)]
n
n
n
n
n
nn[if 992 equals=”Open Access”] Full Text PDF Download[/if 992] n[if 992 not_equal=”Open Access”]
[/if 992]n[if 992 not_equal=”Open Access”] nnn[/if 992]nn[if 379 not_equal=””]n
Browse Figures
n
n[/if 379]n
References
n[if 1104 equals=””]n
- Joselin HGM, Iniyanb S, Sreevalsanc E, Rajapandiand S. A review of wind energy technologies. Renewable and Sustainable Energy Reviews. 2007; 11(6): 1117–1145p.
- National Hydro Power Corporation Limited; http://nhpcindia.com/English/cripts/Hydro_Scenario.aspx.
- Global wind energy council report 2012; http://geospatial.blogs.com/geospatial/2013/08/global- installed-windpower-generation-capacity-reaches-283-gw.html.
- Centre for Wind Energy Technology, India (http://www.cwet.tn.nic.in/html/departments_ewpp.html)
- Frank CW, Bernhard P, Wahl S, Keller JD, Hense A, Susanne The added value of high resolution regional reanalyses for wind power applications. Renewable Energy. 2020; 148: 1094- 1109pp.ISSN 0960-1481, https://doi.org/10.1016/j.renene.2019.09.138.
- Gualtieri G. Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review. Renewable and Sustainable Energy Reviews. 2022; 167:112741, 1-20pp, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2022.112741.
- Lun Isaac YF, Joseph C, Lam Study of Weibull parameters using long-term wind observations. Renewable Energy. 2000; 20: 145-153p.
- Jon Olauson J. ERA5: The new champion of wind power modelling?. Renewable Energy. 2018; 126:322-331pp. ISSN 0960-1481, https://doi.org/10.1016/j.renene.2018.03.056.
- Kwon SD. Uncertainty analysis of wind energy potential assessment. Applied Energy. 2010; 87: 856–865p.
- Khalid Mohamed N, Mohamed S, Hasimah AR. Feasibility assessment of wind energy resources in Malaysia based on NWP models. Renewable Energy. 2014; 62:147-154p. 2014.
- National Institute of Technology Hamirpur @ Centre for Energy and Environment (CEE); http://www.nith.ac.in/energy
- Lashin A, Shata A. An analysis of wind power potential in Port Said, Egypt. Renewable and Sustainable Energy Reviews. 2012; 16:6660–6667p.
- Murthy KSR, Rahi, Estimation of Weibull parameters using graphical method for wind energy applications. Eighteenth National Power Systems Conference (NPSC). 2014; 1-6p. Guwahati, India, doi: 10.1109/NPSC.2014.7103858.
- https://www.lambrecht.net/files/downloadse/LAMBRECHT%20meteo%20Catalogue_EN%2040
_18.pdf.
- Hersbach H, Bell B, Berrisford P. et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146: 1999– 2049pp. https://doi.org/10.1002/qj.3803
- Mirhosseini M, Sharifi F, Sedaghat Assessing the wind energy potential locations in province of Semnan in Iran. Renewable and Sustainable Energy Reviews. 2011;15:449–459p.
- Shata A. Investigation of wind characteristics and wind energy potential at Ras Ghareb, Egypt. Renewable and Sustainable Energy Reviews. 2011; 15:2750– 2755p.
- Mostafaeipour A. Feasibility study of harnessing wind energy for turbine installation in province of Yazd in Iran. Renewable and Sustainable Energy Reviews. 2010; 14:93–111p.
- Zaccheus OO, Folly Statistical Analysis of the Wind Resources at Darling for Energy Production. International Journal of Renewable Energy Research. 2012; 2:250-261p.
- Akdag SA, Dinler A. A new method to estimate Weibull parameters for wind energy applications. Energy Convers Manage. 2009; 50:1761–1766p.
nn[/if 1104][if 1104 not_equal=””]n
- [foreach 1102]n t
- [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
n[/foreach]
n[/if 1104]
nn
nn[if 1114 equals=”Yes”]n
n[/if 1114]
n
n
n
Journal of Alternate Energy Sources & Technologies
n
n
n
n
n
n
| Volume | 15 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | March 26, 2024 | |
| Accepted | April 5, 2024 | |
| Published | April 12, 2024 |
n
n
n
n
n
n function myFunction2() {n var x = document.getElementById(“browsefigure”);n if (x.style.display === “block”) {n x.style.display = “none”;n }n else { x.style.display = “Block”; }n }n document.querySelector(“.prevBtn”).addEventListener(“click”, () => {n changeSlides(-1);n });n document.querySelector(“.nextBtn”).addEventListener(“click”, () => {n changeSlides(1);n });n var slideIndex = 1;n showSlides(slideIndex);n function changeSlides(n) {n showSlides((slideIndex += n));n }n function currentSlide(n) {n showSlides((slideIndex = n));n }n function showSlides(n) {n var i;n var slides = document.getElementsByClassName(“Slide”);n var dots = document.getElementsByClassName(“Navdot”);n if (n > slides.length) { slideIndex = 1; }n if (n (item.style.display = “none”));n Array.from(dots).forEach(n item => (item.className = item.className.replace(” selected”, “”))n );n slides[slideIndex – 1].style.display = “block”;n dots[slideIndex – 1].className += ” selected”;n }n”}]
