Wind speed distribution modeling for wind power estimation: Case of Agadir in Morocco

2018 ◽  
Vol 43 (2) ◽  
pp. 190-200 ◽  
Author(s):  
Ijjou Tizgui ◽  
Fatima El Guezar ◽  
Hassane Bouzahir ◽  
Brahim Benaid
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Wind Power ◽  
Goodness Of Fit ◽  
Statistical Tests ◽  
A Priori ◽  
Speed Distribution ◽  
Distribution Modeling ◽  
Wind Speed Distribution

To estimate a wind turbine output, optimize its dimensioning, and predict the economic profitability and risks of a wind energy project, wind speed distribution modeling is crucial. Many researchers use directly Weibull distribution basing on a priori acceptance. However, Weibull does not fit some wind speed regimes. The goal of this work is to model the wind speed distribution at Agadir. For that, we compare the accuracy of four distributions (Weibull, Rayleigh, Gamma, and lognormal) which have given good results in this yield. The goodness-of-fit tests are applied to select the effective distribution. The obtained results explain that Weibull distribution is fitting the histogram of observations better than the other distributions. The analysis deals with comparing the error in estimating the annual wind power density using the examined distributions. It was found that Weibull distribution presents minimum error. Thus, wind energy assessors in Agadir can use directly Weibull distribution basing on a scientific decision made via statistical tests. Moreover, assessors worldwide can use the followed methodology to model their wind speed measurements.

Study of Combined Forecast Model of Wind Power Output Based on GM-Weibull Wind Speed Distribution

2014 ◽  
Vol 670-671 ◽  
pp. 1566-1569
Author(s):  
Yun Teng ◽  
Qian Hui ◽  
Xin Yu ◽  
Zheng Liu ◽  
Yong Gang Zhang
Keyword(s):  
Wind Speed ◽  
Weibull Distribution ◽  
Wind Power ◽  
Power Output ◽  
Wind Farm ◽  
Distribution Model ◽  
Grey Theory ◽  
Grey Prediction ◽  
Speed Distribution ◽  
Wind Speed Distribution

The grey theory is employed to establish the grey prediction-wind speed Weibull distribution model and calculate the Weibull distribution parameters according to the randomness and intermittence of the wind power output. The wind speed distribution of the wind farm and the effective wind power density are predicted accurately, the wind power and the electric fan efforts in generating capacity and other important data can be obtained according to the actual terrain wind farm wind speed data.

scholarly journals An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3063 ◽  
Author(s):  
Krishnamoorthy R ◽  
Udhayakumar K ◽  
Kannadasan Raju ◽  
Rajvikram Madurai Elavarasan ◽  
Lucian Mihet-Popa
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Likelihood Method ◽  
Wind Speeds ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
South West Monsoon ◽  
Wind Distribution ◽  
Wind Resource

Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum–Sandras, and Bimodal–Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving complex problems and parameter estimations. The data analytics are carried out in the MATLAB platform, with in-house coding developed for MFO parameters estimated through optimization and other wind distribution parameters using the maximum likelihood method. The observed outcomes show that the MFO method performed well on parameter estimation. Correspondingly, wind power generation was shown to peak at the South West Monsoon periods from June to September, with mean wind speeds ranging from 9 to 12 m/s. Furthermore, the wind speed distribution method of mixed Weibull, Nakagami, and Rician methods performed well in calculating potential assessments for the targeted locations. Likewise, the Gulf of Khambhat (offshore) area has steady wind speeds ranging from 7 to 10 m/s with less turbulence intensity and the highest wind power density of 431 watts/m2. The proposed optimization method proves its potential for accurate assessment of Indian wind conditions in selected locations.

Analysis of the Technical Features of Wind Energy

2013 ◽  
Vol 805-806 ◽  
pp. 420-423
Author(s):  
Guan Jun Ding ◽  
Bang Kui Fan ◽  
Teng Long ◽  
Hai Bin Lan ◽  
Jing Wang
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Power Curve ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
Key Factor ◽  
Mean Wind Speed ◽  
Technical Features ◽  
Power And Energy

Along with the concern about environmental pollution and global warming, the development of wind energy has rapidly progressed over the last decade by the improving in the technology and the provision of government energy policy. In view of the intermittent property of wind energy causing variability, unpredictability and uncertainty, this paper analyzes the related technical features of wind energy, e.g., power curve, wind speed, wind power and energy, to provide the further reference for analyzing the impacts of wind energy on power system in depth. First of all, wind turbine, the key part of wind energy, is discussed, including its components and power curve. Second, wind speed, the key factor for calculating wind power and energy, is analyzed and derived in detail. On the basis of wind speed distribution, two types of wind speed are calculated, i.e., the arithmetic mean wind speed and the cubic root cube wind speed. Then, wind power and energy are presented and calculated. Finally, the related conclusions are drawn.

scholarly journals Inverse Weibull Method Application to wind Speed Modeling in Campo Grande-Ms Brazil

2019 ◽  
Vol 6 ◽  
Author(s):  
Razika Ihaddadene ◽  
Nabila Ihaddadene ◽  
Amaury de Souza ◽  
Abdelhadi Beghidja
Keyword(s):  
Wind Speed ◽  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Efficient Estimation ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
Wind Potential ◽  
Inverse Weibull Distribution

Wind potential estimation requires an analysis of wind characteristics (wind speed density and wind direction). In this study, the applicability of two distribution models named Weibull and Inverse Weibull aiming to characterize the wind speed distribution in Campo Grande-Ms (Brazil) is investigated. The wind speed data collected from Campo Grande-Ms National Institute of Meteorology (INMET) at 10 m height for 5 years from January 2013 to December 2017, at an hour interval, are used. The method of maximum likelihood estimation is applied to calculate the parameters of the selected distributions. The best distribution function is chosen based on three goodness-of-fit statistics, namely; mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R²). The obtained results indicate that the Weibull distribution provides a more accurate and efficient estimation than Inverse Weibull distribution. Therefore, Weibull distribution can be used to better estimate wind speed distribution in Campo Grande-Ms (Brazil) than Inverse Weibull distribution.

Wind Energy Potential in Nine Coastal Sites in Malaysia

2013 ◽  
Vol 1 (1) ◽  
pp. 10-15
Author(s):  
Kamaruzzaman Sopian ◽  
Tamer Khatib
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Data Bank ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Speed Distribution ◽  
Average Wind ◽  
Wind Speed Distribution ◽  
Wind Energy Potential ◽  
Swept Area

 In this paper, the wind energy potential in Malaysia is examined by analyzing hourly wind speed data for nine coastal sites namely Bintulu, Kota Kinabalu, Kuala Terengganu, Kuching, Kudat, Mersing, Sandakan, Tawau and Pulau Langkawi. The monthly averages of wind speed and wind energy are calculated. Moreover, the wind speed distribution histogram is constructed for these sites. The results showed that the average wind speed for these sites is in the range of (1.8-2.9) m/s while the annual energy of the wind hitting a wind turbine with a 1 m2 swept area is in the range of (15.4-25.2) kWh/m2.annum. This paper provides a data bank for wind energy for Malaysia.

Wind Energy Potential in Nine Coastal Sites in Malaysia

2013 ◽  
Vol 1 (1) ◽  
pp. 10-15
Author(s):  
Kamaruzzaman Sopian ◽  
Tamer Khatib
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Data Bank ◽  
Energy Potential ◽  
Average Wind Speed ◽  
Speed Distribution ◽  
Average Wind ◽  
Wind Speed Distribution ◽  
Wind Energy Potential ◽  
Swept Area

 In this paper, the wind energy potential in Malaysia is examined by analyzing hourly wind speed data for nine coastal sites namely Bintulu, Kota Kinabalu, Kuala Terengganu, Kuching, Kudat, Mersing, Sandakan, Tawau and Pulau Langkawi. The monthly averages of wind speed and wind energy are calculated. Moreover, the wind speed distribution histogram is constructed for these sites. The results showed that the average wind speed for these sites is in the range of (1.8-2.9) m/s while the annual energy of the wind hitting a wind turbine with a 1 m2 swept area is in the range of (15.4-25.2) kWh/m2.annum. This paper provides a data bank for wind energy for Malaysia.

Application of differential evolution for wind speed distribution parameters estimation

2021 ◽  
pp. 0309524X2199996
Author(s):  
Rajesh Kumar ◽  
Arun Kumar
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Wind Speed ◽  
Weibull Distribution ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Speed Distribution ◽  
De Algorithm ◽  
Wind Speed Distribution ◽  
Distribution Parameters

Weibull distribution is an extensively used statistical distribution for analyzing wind speed and determining energy potential studies. Estimation of the wind speed distribution parameter is essential as it significantly affects the success of Weibull distribution application to wind energy. Various estimation methods viz. graphical method, moment method (MM), maximum likelihood method (ML), modified maximum likelihood method, and energy pattern factor method or power density method have been presented in various reported research studies for accurate estimation of distribution parameters. ML is the most preferred approach to study the parameter estimation. ML works on the principle of forming a likelihood function and maximizing the function for parameter estimation. ML generally uses the numerical based iterative method, such as Newton–Raphson. However, the iterative methods proposed in the literature are generally computationally intensive. In this paper, an efficient technique utilizing differential evolution (DE) algorithm to enhance the estimation accuracy of maximum likelihood estimation has been presented. The [Formula: see text] of GA-Weibull, SA-Weibull, and DE-Weibull is 0.958, 0.953, and 0.973 respectively, and value of RMSE of DE-Weibull 0.0083, GA-Weibull (0.0104), and SA-Weibull (0.0110), for the yearly wind speed data are obtained. The lowest root mean square error and larger regression value for both monthly and yearly wind speed data indicate that the DE-Weibull distribution has the best goodness of fit and advocate the DE algorithm for the parameter estimation.

scholarly journals A Comparative Study of Different Wind Speed Distribution Models for Accurate Evaluation of Onshore Wind Energy Potential: A Case Study on the Southern Coasts of Iran

2020 ◽  
Author(s):  
Siyavash Filom ◽  
Soheil Radfar ◽  
Roozbeh Panahi
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Weibull Function ◽  
Laboratory System ◽  
Energy Potential ◽  
Onshore Wind ◽  
Accurate Evaluation ◽  
Speed Distribution ◽  
Wind Speed Distribution ◽  
Wind Energy Potential

Wind power output is highly dependent on the wind speed at the selected site, therefore wind-speed distribution modeling is the most important step in the assessment of wind energy potential. This study aims at accurate evaluation of onshore wind energy potential in seven coastal cities in the south of Iran. Six Probability Distribution Functions (PDFs) were examined over representative stations. It has been deduced that the Weibull function, which was the most used PDF in similar studies, was only applicable to one station. Here, Gamma offered the best fit for three stations and for the other ones, Generalized Extreme Value (GEV) performed better. Considering the ranking of six examined PDFs and the simplicity of Gamma, it was identified as the effective function in the southern coasts of Iran bearing in mind the geographic distribution of stations. Besides, six turbine power curve functions were contributed to investigate the capacity factor. That was very important, as using only one function could cause under- or over-estimation. Then, stations were classified based on the National Renewable Energy Laboratory system. Last but not least, examining a range of wind turbines enabled scholars to extend this study into the practice and prioritize development of stations considering budget limits.

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