An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods
Wind direction and speed are both crucial factors for wind farm layout; however, the relationship between the two factors has not been well addressed. To optimize wind farm layout, this study aims to statistically explore wind speed characteristics under different wind directions and wind direction...
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doaj-ea38c10f725a45feaf179bc291dc9f712020-11-24T22:52:12ZengMDPI AGSustainability2071-10502018-11-011012433810.3390/su10124338su10124338An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution MethodsLidong Zhang0Qikai Li1Yuanjun Guo2Zhile Yang3Lei Zhang4School of Energy and Power Engineering, Northeast Electric Power University, Jilin 132012, ChinaSchool of Energy and Power Engineering, Northeast Electric Power University, Jilin 132012, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaChina Datang Corporation Renewable Science and Technology Research Institute, Beijing 10052, ChinaWind direction and speed are both crucial factors for wind farm layout; however, the relationship between the two factors has not been well addressed. To optimize wind farm layout, this study aims to statistically explore wind speed characteristics under different wind directions and wind direction characteristics. For this purpose, the angular⁻linear model for approximating wind direction and speed characteristics were adopted and constructed with specified marginal distributions. Specifically, Weibull⁻Weibull distribution, lognormal⁻lognormal distribution and Weibull⁻lognormal distribution were applied to represent the marginal distribution of wind speed. Moreover, the finite mixture of von Mises function (FVMF) model was used to investigate the marginal distribution of wind direction. The parameters of those models were estimated by the expectation⁻maximum method. The optimal model was obtained by comparing the coefficient of determination value (<i>R</i><sup>2</sup>) and Akaike’s information criteria (AIC). In the numerical study, wind data measured at a featured wind farm in north China was adopted. Results showed that the proposed joint distribution function could accurately represent the actual wind data at different heights, with the coefficient of determination value (<i>R</i><sup>2</sup>) of 0.99.https://www.mdpi.com/2071-1050/10/12/4338wind characteristicsjoint probability distributionwind direction and speedWeibull–Weibull distributionlognormal–lognormal distributionWeibull–lognormal distribution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lidong Zhang Qikai Li Yuanjun Guo Zhile Yang Lei Zhang |
spellingShingle |
Lidong Zhang Qikai Li Yuanjun Guo Zhile Yang Lei Zhang An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods Sustainability wind characteristics joint probability distribution wind direction and speed Weibull–Weibull distribution lognormal–lognormal distribution Weibull–lognormal distribution |
author_facet |
Lidong Zhang Qikai Li Yuanjun Guo Zhile Yang Lei Zhang |
author_sort |
Lidong Zhang |
title |
An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods |
title_short |
An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods |
title_full |
An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods |
title_fullStr |
An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods |
title_full_unstemmed |
An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods |
title_sort |
investigation of wind direction and speed in a featured wind farm using joint probability distribution methods |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2018-11-01 |
description |
Wind direction and speed are both crucial factors for wind farm layout; however, the relationship between the two factors has not been well addressed. To optimize wind farm layout, this study aims to statistically explore wind speed characteristics under different wind directions and wind direction characteristics. For this purpose, the angular⁻linear model for approximating wind direction and speed characteristics were adopted and constructed with specified marginal distributions. Specifically, Weibull⁻Weibull distribution, lognormal⁻lognormal distribution and Weibull⁻lognormal distribution were applied to represent the marginal distribution of wind speed. Moreover, the finite mixture of von Mises function (FVMF) model was used to investigate the marginal distribution of wind direction. The parameters of those models were estimated by the expectation⁻maximum method. The optimal model was obtained by comparing the coefficient of determination value (<i>R</i><sup>2</sup>) and Akaike’s information criteria (AIC). In the numerical study, wind data measured at a featured wind farm in north China was adopted. Results showed that the proposed joint distribution function could accurately represent the actual wind data at different heights, with the coefficient of determination value (<i>R</i><sup>2</sup>) of 0.99. |
topic |
wind characteristics joint probability distribution wind direction and speed Weibull–Weibull distribution lognormal–lognormal distribution Weibull–lognormal distribution |
url |
https://www.mdpi.com/2071-1050/10/12/4338 |
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