scholarly journals Optimum Selection of Wind Turbines Using Normalized Power and Capacity Factor Curves

2021 ◽  
pp. 2813-2823
Author(s):  
Firas A. Hadi ◽  
Zaid F. Makki ◽  
Rafa A. Al-Baldawi
Keyword(s):  
Wind Turbine ◽  
Performance Index ◽  
Shape Parameter ◽  
Parameters Estimation ◽  
Capacity Factor ◽  
Turbine Performance ◽  
Optimum Selection ◽  
Novel Method ◽  
A Site ◽  
Selection Of

The main objective of this paper is present a novel method to choice a certain wind turbine for a specific site by using normalized power and capacity factor curves. The site matching is based on identifying the optimum turbine rotation speed parameters from turbine performance index (TPI) curve, which is obtained from the higher values of normalized power and capacity factor curves. Wind Turbine Performance Index a new ranking parameter, is defined to optimally match turbines to wind site. The relations (plots) of normalized power, capacity factor, and turbine performance index versus normalized rated wind speed are drawn for a known value of Weibull shape parameter of a site, thus a superior method is used for Weibull parameters estimation which is called Equivalent Energy Method (EEM).

Wind Energy Potential Assessment and Wind Turbine Performance Investigation in the Cotonou Coast (Benin Republic)

2019 ◽  
Vol 45 ◽  
pp. 89-98
Author(s):  
Maurel Aza-Gnandji ◽  
François Xavier Fifatin ◽  
Frédéric Dubas ◽  
Christophe Espanet ◽  
Antoine Vianou
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Maximum Energy ◽  
Capacity Factor ◽  
Energy Potential ◽  
Turbine Performance ◽  
Best Fitting ◽  
Wind Energy Potential ◽  
Wind Resource ◽  
Benin Republic

This paper presents a study of the monthly variability of wind energy potential at several heights and an investigation of the best fitting commercial wind turbine in the Cotonou coast (Benin Republic). The monthly Weibull parameters are calculated at 10 m and extrapolated at 30 and 50 m heights. The monthly Weibull wind power density and the wind speed carrying maximum energy are calculated at 10, 30 and 50 m. We showed that wind resource in the Cotonou coast is favorable for wind energy production at 30 and 50 m heights. The capacity factor of selected commercial wind turbines is calculated to investigate the best fitting wind turbine in the Cotonou coast. It turns out that Polaris 19-50 is the best fitting wind turbine in the selected turbines with a mean capacity factor of 0.49.

Optimum Selection of a Wind Turbine Generator System

1979 ◽  
Vol 3 (3) ◽  
pp. 145-150
Author(s):  
J. K. Shultis ◽  
L. A. Poch ◽  
N. D. Eckhoff
Keyword(s):  
Wind Turbine ◽  
Turbine Generator ◽  
Generator System ◽  
Wind Turbine Generator ◽  
Optimum Selection ◽  
Wind Turbine Generator System ◽  
Selection Of

Alternative Approach to Wind Turbine Performance Index Assessment

2014 ◽  
Vol 140 (4) ◽  
pp. 06014001
Author(s):  
Y. Ditkovich ◽  
A. Kuperman ◽  
A. Yahalom ◽  
M. Byalsky
Keyword(s):  
Wind Turbine ◽  
Performance Index ◽  
Turbine Performance ◽  
Alternative Approach

Wind turbine performance index

2012 ◽  
Author(s):  
Yuri Ditkovich ◽  
Alon Kuperman ◽  
Asher Yahalom ◽  
Michael Byalsky ◽  
Yael Ditkovich ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Performance Index ◽  
Turbine Performance

scholarly journals The effect of wind direction shear on turbine performance in a wind farm in central Iowa

2019 ◽  
Author(s):  
Miguel Sanchez Gomez ◽  
Julie K. Lundquist
Keyword(s):  
Wind Turbine ◽  
Wind Direction ◽  
Wind Shear ◽  
Wind Farm ◽  
Power Production ◽  
Atmospheric Conditions ◽  
Wind Speeds ◽  
Turbine Performance ◽  
A Site ◽  
Future Work

Abstract. Numerous studies have shown that atmospheric conditions affect wind turbine performance, however, some findings have exposed conflicting results for different locations and diverse analysis methodologies. In this study, we explore how the change in wind direction with height (direction wind shear), a site-differing factor between conflicting studies, affects wind turbine performance. We utilized lidar and turbine data collected from the 2013 Crop Wind Energy eXperiment (CWEX) project between June and September in a wind farm in north-central Iowa. Directional wind shear was found to follow a diurnal cycle and to monotonically decrease with increasing wind speeds. Using different thresholds to distinguish between high- and low-directional wind shear scenarios, we found that larger thresholds evidence statistically-significant effects on turbine power production for lower wind speeds. We further analyzed a threshold of 0.225 deg m−1 and found turbine underperformance in the order of 10 % for wind speed regimes below 8 m s−1. Considering a time period of ramping electricity demand (05:30–09:00 LT) exposed the fact that large direction shear occurs during this time and is undermining turbine performance by more than 10 %. A predominance of clockwise direction shear (wind veering) cases compared to counterclockwise (wind backing) was also observed throughout the campaign. Moreover, large veering was found to have greater detrimental effects on turbine performance compared to small backing values. This study shows that changes in wind direction with height should be considered when analyzing turbine performance, however, future work on segregating speed and direction shear should be pursued to quantify the effects of only one factor on turbine power production.

Wind turbine rank method for a wind park scenario

2016 ◽  
Vol 13 (6) ◽  
pp. 500-508 ◽  
Author(s):  
Mohd Zamri Ibrahim ◽  
Aliashim Albani
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Capital Cost ◽  
Capacity Factor ◽  
Content Type ◽  
Case Study Site ◽  
Wind Park ◽  
A Site ◽  
Turbine Installation

Purpose This paper aims to present a method of the wind turbine ranking, either stall or pitch-regulated wind turbine (WTG), to determine the suitability of wind turbine in a selected site. Design/methodology/approach The method included the wind park target capacity, the maximum hub-height, the standard rotor diameter and the characteristic of wind speed on the site. As the method had been applied to a wind park, with more than one wind turbine, the wake losses had been considered by subtracting the gross capacity factor. Besides, the turbine-site matching index (TSMI) was computed by dividing the net capacity factor with the total installed capital cost per kilowatt. Findings The components of the total installed capital cost were cost of turbine, installation, as well as operation and maintenance. Meanwhile, the target capacity index (TCI) was calculated by dividing the estimated wind park capacity with the target wind park capacity. Originality/value Both TSMI and TCI were used together to rank the wind turbines. Furthermore, a site in the eastern part of Kudat was selected as the case study site, where ten models of wind turbines were tested and ranked.

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