Investigation of wind resource characteristics in mountain wind farm using multiple-unit SCADA data in Chenzhou: A case study

Abstract Wind resource characteristics in a mountain wind farm is more complicated and changeable than that in a flat wind farm due to the influence of complex mountain terrain-topography on air flow. Although there have been a large number of investigations on wind resource characteristics, the local wind resource characteristics in a mountain wind farm are still a lack. To fill this knowledge gap, high-resolution wind data are extracted for investigating from multiple-unit SCADA data, that is, wind data are collected from four selected wind turbines with SCADA system, which means wind data are from four different locations. For each location, more than thirty million sets of wind data are extracted for investigation in 2015. Then, several kinds of probability density functions (PDFs) are compared, and one-dimensional and multidimensional kernel density estimation method is selected for the investigation of the frequency distribution. Data preprocessing methods of both wind speed and wind direction are also presented. Finally, the results of this investigation reveal the specific change characteristics of wind speed and wind direction in the investigated mountain wind farm. Yearly mean wind speeds for four different locations are 3.8 m/s, 5.1 m/s, 5.4 m/s and 5.6 m/s, respectively.

[1]  S. H. Pishgar-Komleh,et al.  Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran) , 2015 .

[2]  Miguel Becerra,et al.  Wind energy potential in Chile: Assessment of a small scale wind farm for residential clients , 2017 .

[3]  W. R. Hargraves,et al.  Methods for Estimating Wind Speed Frequency Distributions. , 1978 .

[4]  Jason R. Marden,et al.  Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study , 2016 .

[5]  Riccardo Burlon,et al.  Joint probability distributions for wind speed and direction. A case study in Sicily , 2015, 2015 International Conference on Renewable Energy Research and Applications (ICRERA).

[6]  Kit Po Wong,et al.  Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.

[7]  Ömer Nezih Gerek,et al.  A novel wind speed modeling approach using atmospheric pressure observations and hidden Markov models , 2010 .

[8]  Xiaofu Xiong,et al.  Estimating wind speed probability distribution using kernel density method , 2011 .

[9]  J. A. Carta,et al.  Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions , 2007 .

[10]  A. Hepbasli,et al.  A review on the development of wind energy in Turkey , 2004 .

[11]  A. Sahin,et al.  Wind power energy potential at the northeastern region of Saudi Arabia , 1998 .

[12]  N. Vogiatzis,et al.  Analysis of wind potential and characteristics in North Aegean, Greece , 2004 .

[13]  E. Erdem,et al.  Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites , 2010 .

[14]  E. Akpinar.  A Statistical Investigation of Wind Energy Potential , 2006 .

[15]  M. C. Jones,et al.  A reliable data-based bandwidth selection method for kernel density estimation , 1991 .

[16]  Matthew A. Lackner,et al.  Probability distributions for offshore wind speeds , 2009 .

[17]  Kasra Mohammadi,et al.  Evaluating the suitability of wind speed probability distribution models: A case of study of east and southeast parts of Iran , 2016 .

[18]  D. Weisser,et al.  A wind energy analysis of Grenada: an estimation using the 'Weibull' density function , 2003 .

[19]  A. Brett,et al.  The Characteristics of Wind Velocity that Favor the Fitting of a Weibull Distribution in Wind Speed Analysis , 1984 .

[20]  Irène Gijbels,et al.  Practical bandwidth selection in deconvolution kernel density estimation , 2004, Comput. Stat. Data Anal..

[21]  A. Celik.  A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey , 2004 .

[22]  Xianguo Li,et al.  MEP-type distribution function: a better alternative to Weibull function for wind speed distributions , 2005 .

[23]  A. Messac,et al.  A Multivariate and Multimodal Wind Distribution model , 2013 .

[24]  T. W. Lambert,et al.  Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis , 2000 .

[25]  Carla Freitas de Andrade,et al.  Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil , 2012 .

[26]  Gong Li,et al.  Application of Bayesian model averaging in modeling long-term wind speed distributions , 2010 .

[27]  A. Jimoh,et al.  Wind distribution and capacity factor estimation for wind turbines in the coastal region of South Africa , 2012 .

[28]  José Guerra,et al.  Definition, analysis and experimental investigation of operation modes in hydrogen-renewable-based power plants incorporating hybrid energy storage , 2016 .

[29]  Tarn Duong,et al.  ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R , 2007 .

[30]  T. Chang.  Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application , 2011 .

[31]  H. S. Bagiorgas,et al.  Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean , 2010 .

[32]  Mehmet Bilgili,et al.  THE WIND POWER POTENTIAL OF THE EASTERN MEDITERRANEAN REGION OF TURKEY , 2005 .

[33]  Takvor H. Soukissian,et al.  Use of multi-parameter distributions for offshore wind speed modeling: The Johnson SB distribution , 2013 .

[34]  D. Kammen,et al.  Where, when and how much wind is available? A provincial-scale wind resource assessment for China , 2014 .

[35]  E. Akpinar,et al.  A statistical analysis of wind speed data used in installation of wind energy conversion systems , 2005 .

[36]  Sándor Baran,et al.  Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components , 2013, Comput. Stat. Data Anal..

[37]  Piotr Wais,et al.  Two and three-parameter Weibull distribution in available wind power analysis , 2017 .

[38]  A. M. Razali,et al.  The Probability Distribution Model of Wind Speed over East Malaysia , 2013 .

[39]  W. E. Bardsley.  Note on the Use of the Inverse Gaussian Distribution for Wind Energy Applications , 1980 .

[40]  Andrew Ning,et al.  Wind plant system engineering through optimization of layout and yaw control , 2016 .

[41]  J. Dai,et al.  Power fluctuation evaluation of large-scale wind turbines based on SCADA data , 2017 .

[42]  S. Asgarpoor,et al.  Short and long-term reliability assessment of wind farms , 2010, North American Power Symposium 2010.

[43]  Fawzi A. L. Jowder,et al.  Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain , 2009 .

[44]  S. Baran,et al.  Log‐normal distribution based Ensemble Model Output Statistics models for probabilistic wind‐speed forecasting , 2014, 1407.3252.

[45]  Y. Noorollahi,et al.  Using artificial neural networks for temporal and spatial wind speed forecasting in Iran , 2016 .

[46]  Y. Kantar,et al.  Analysis of wind speed distributions: Wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function , 2008 .

[47]  Omid Nematollahi,et al.  Energy demands and renewable energy resources in the Middle East , 2016 .

[48]  Yeliz Mert Kantar,et al.  Use of MinMaxEnt distributions defined on basis of MaxEnt method in wind power study , 2008 .

[49]  J. A. Carta,et al.  A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands , 2009 .

[50]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[51]  Blaise Sheridan,et al.  Calculating the offshore wind power resource: Robust assessment methods applied to the U.S. Atlantic Coast , 2012 .

[52]  Ramazan Köse,et al.  The analysis of wind data and wind energy potential in Kutahya, Turkey , 2004 .