Nonlinear approximation of wind turbine capacity factor under Rayleigh winds

2013 ◽  
Vol 24 (12) ◽  
pp. 1818-1821
Author(s):  
Yuri Ditkovich ◽  
Gilad Zangwill ◽  
Alon Kuperman
Keyword(s):  
Wind Turbine ◽  
Nonlinear Approximation ◽  
Capacity Factor ◽  
Turbine Capacity

scholarly journals Comparison of Three Methods for Wind Turbine Capacity Factor Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Y. Ditkovich ◽  
A. Kuperman
Keyword(s):  
Wind Turbine ◽  
Nonlinear Approximation ◽  
Capacity Factor ◽  
Valuable Insight ◽  
Power Curve ◽  
Average Wind Speed ◽  
Turbine Performance ◽  
Average Wind ◽  
Turbine Capacity

Three approaches to calculating capacity factor of fixed speed wind turbines are reviewed and compared using a case study. The first “quasiexact” approach utilizes discrete wind raw data (in the histogram form) and manufacturer-provided turbine power curve (also in discrete form) to numerically calculate the capacity factor. On the other hand, the second “analytic” approach employs a continuous probability distribution function, fitted to the wind data as well as continuous turbine power curve, resulting from double polynomial fitting of manufacturer-provided power curve data. The latter approach, while being an approximation, can be solved analytically thus providing a valuable insight into aspects, affecting the capacity factor. Moreover, several other merits of wind turbine performance may be derived based on the analytical approach. The third “approximate” approach, valid in case of Rayleigh winds only, employs a nonlinear approximation of the capacity factor versus average wind speed curve, only requiring rated power and rotor diameter of the turbine. It is shown that the results obtained by employing the three approaches are very close, enforcing the validity of the analytically derived approximations, which may be used for wind turbine performance evaluation.

scholarly journals Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010)

Energy ◽  
2021 ◽  
Vol 226 ◽  
pp. 120364
Author(s):  
Sheila Carreno-Madinabeitia ◽  
Gabriel Ibarra-Berastegi ◽  
Jon Sáenz ◽  
Alain Ulazia
Keyword(s):  
Wind Turbine ◽  
Iberian Peninsula ◽  
Wind Power ◽  
Power Density ◽  
Capacity Factor ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Long Term Changes ◽  
Turbine Capacity

A new semi-empirical wind turbine capacity factor for maximizing annual electricity and hydrogen production

2020 ◽  
Vol 45 (32) ◽  
pp. 15888-15903 ◽  
Author(s):  
Ahmad Sedaghat ◽  
Ali Mostafaeipour ◽  
Mostafa Rezaei ◽  
Mehdi Jahangiri ◽  
Amirreza Mehrabi
Keyword(s):  
Hydrogen Production ◽  
Wind Turbine ◽  
Capacity Factor ◽  
Turbine Capacity ◽  
Semi Empirical

An Investigation of the Low Performance of the First Wind Farm in Thailand: A Case of Poor Wind Turbine-Site Matching

2013 ◽  
Vol 724-725 ◽  
pp. 469-475
Author(s):  
Akraphon Janon ◽  
Tanakorn Wongwuttanasatian ◽  
Gumphol Faikaow ◽  
Panumas Srinor
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Performance Indicator ◽  
Wind Farms ◽  
Measured Data ◽  
Capacity Factor ◽  
Contributing Factors ◽  
Short Period ◽  
Low Performance ◽  
Turbine Capacity

This research investigates causes of the low performance of the first commercial wind farm in Thailand. The measured data suggests that this wind farm is uncompetitive. We found that this is due to poor turbine-site matching. In contrary to a traditionally held belief, the hub-height and turbine capacity are not the contributing factors. Key performance indicators are obtained for use as benchmarks in future wind farm appraisal. Then a turbine selection method is proposed to increase the capacity factor (CF) of the wind farm. CF is used as the main performance indicator, which can be compared to other wind farms. The real capacity factor (CFR) determined using measured data is 14.90%. This CFR is considerably lower than the estimated capacity factor (CFE) of 21.53%. The low CFR is due to grid instability. In addition, the CFR is lower than the CFE by a factor of 0.69. This information is valuable to investors and wind farm developers in a wind farm feasibility study. A graphical wind turbine-site matching is proposed. Wind turbine-site matching is achieved by using normalised power output plots and power density plots on a probability density graph of the wind site. This process consumes a short period of time. An improved turbine-site matching is achieved.

Hub height optimisation of commercial WTGs based on accurate wind resource analysis

2021 ◽  
pp. 0309524X2110227
Author(s):  
Kyle O Roberts ◽  
Nawaz Mahomed
Keyword(s):  
Power Generation ◽  
Wind Turbine ◽  
Wind Power ◽  
Capacity Factor ◽  
Resource Analysis ◽  
Wind Speeds ◽  
Unique Maximum ◽  
Wind Measurements ◽  
Generation Capacity ◽  
Wind Resource

Wind turbine selection and optimal hub height positioning are crucial elements of wind power projects. However, in higher class wind speeds especially, over-exposure of wind turbines can lead to a reduction in power generation capacity. In this study, wind measurements from a met mast were validated according to specifications issued by IRENA and NREL. As a first step, it is shown that commercial WTGs from a database may be matched to the wind class and turbulence intensity. Secondly, a wind turbine selection algorithm, based on maximisation of capacity factor, was implemented across the range of WTGs. The selected WTGs were further exposed to an iterative algorithm using pointwise air density and wind shear coefficients. It is shown that a unique maximum capacity factor, and hence wind power generation, exists for a wind turbine, premised on its eventual over-exposure to the wind resource above a certain hub height.

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).

A novel fractional discrete nonlinear grey Bernoulli model for forecasting the wind turbine capacity of China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wen-ze Wu ◽  
Wanli Xie ◽  
Chong Liu ◽  
Tao Zhang
Keyword(s):  
Wind Turbine ◽  
Nonlinear Coefficient ◽  
Predictive Performance ◽  
Prediction Performance ◽  
New Method ◽  
Grey Model ◽  
Content Type ◽  
Proposed Model ◽  
Whale Optimization ◽  
Turbine Capacity

PurposeA new method for forecasting wind turbine capacity of China is proposed through grey modelling technique.Design/methodology/approachFirst of all, the concepts of discrete grey model are introduced into the NGBM(1,1) model to reduce the discretization error from the differential equation to its discrete forms. Then incorporating the conformable fractional accumulation into the discrete NGBM(1,1) model is carried out to further improve the predictive performance. Finally, in order to effectively seek the emerging coefficients, namely, fractional order and nonlinear coefficient, the whale optimization algorithm (WOA) is employed to determine the emerging coefficients.FindingsThe empirical results show that the newly proposed model has a better prediction performance compared to benchmark models; the wind turbine capacity from 2019 to 2021 is expected to reach 275954.42 Megawatts in 2021. According to the forecasts, policy suggestions are provided for policy-makers.Originality/valueBy combing the fractional accumulation and the concepts of discrete grey model, a new method to improve the prediction performance of the NGBM(1,1) model is proposed. The newly proposed model is firstly applied to predict wind turbine capacity of China.

Estimating the potential of wind energy resources using Weibull parameters: A case study of the coastline region of Dar es Salaam, Tanzania

2021 ◽  
Vol 11 (1) ◽  
pp. 1093-1104
Author(s):  
Enock Michael ◽  
Dominicus Danardono Dwi Prija Tjahjana ◽  
Aditya Rio Prabowo
Keyword(s):  
Parameter Estimation ◽  
Wind Energy ◽  
Wind Turbine ◽  
Cost Effective ◽  
Dar Es Salaam ◽  
Capacity Factor ◽  
Energy Potential ◽  
Weibull Parameters ◽  
Distribution Parameter Estimation ◽  
Annual Capacity

Abstract This study aimed to compare the graphical method (GM) and standard deviation method (SDM), based on analyses and efficient Weibull parameters by estimating future wind energy potential in the coastline region of Dar es Salaam, Tanzania. Hence, the conclusion from the numerical method comparisons will also determine suitable wind turbines that are cost-effective for the study location. The wind speed data for this study were collected by the Tanzania Meteorological Authority Dar es Salaam station over the period of 2017 to 2019. The two numerical methods introduced in this study were both found to be appropriate for Weibull distribution parameter estimation in the study area. However, the SDM gave a higher value of the Weibull parameter estimation than the GM. Furthermore, the five selected commercial wind turbine models that were simulated in terms of performance were based on a capacity factor using the SDM and were both over 25% the recommended capacity factor value. The Polaris P50-500 commercial wind turbine is recommend as a suitable wind turbine to be installed in the study area due to its maximum annual capacity factor value over 3 years.

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