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International Journal of wind and Renewable Energy Volume 3 Issue 2 (pp, 26-38), ISSN: 2277-3975

Wind Resource Estimation Techniques-An Overview Lalit Anjum1 1.

Centre for Energy and Environment (Alumni), National Institute of Technology, Hamirpur H.P.177005 India

Abstract- A review of the wind resource assessment methodologies and an overview of numerical models are undertaken in this paper with stress laid on the preliminary estimation using local trees and vegetation as biological indicators of wind potential at site. Present review consists of the various methods and strategies used by the earlier researchers for assessing the wind resource at any region. Besides, the utilisation of advanced numerical weather prediction models is also discussed. Further several regional case studies are also presented which have been done using mesoscale and other wind climate prediction models worldwide. Moreover, the wind shear profile estimation using linear and non-linear models are also described. Also the review of the comparison studies between WAsP and CFD mesoscale models in evaluating the wind profiles at different locations is been done. Keywords – Review, Wind resource assessment, Case studies, Statistical tools, Vertical Shear

I. INTRODUCTION Energy has been the mainstay of the technical as well as commercial growth of the world economies whether emerging or developed ones. With the over exploitation of the conventional energy sources the world has gradually moved to its dead end. The environmental degradation and climate change are the core concerns in today’s world where each and every individual is interdependent on one another. The evolution and development of renewable energies as the alternative way of energy generation has paved a new way for the sustainable sources of energy in the coming future. Amongst the various renewable energy options, wind energy has emerged as a viable, cost-effective and commercial option for grid connected power generation. During the past quarter of a century, a significant thrust has been given to the development, trial and induction of wind energy technology for use in different sectors as pointed in Akshay Urja [1]. In India, which still draws its lion’s share of energy supply from conventional sources of thermal, oil and gas, the grid connected wind power generation has gained a high level of attention and acceptability as compared to other renewable technologies available in the country. Wind energy installation in the country is around 11,807 MW as of March 2010 and around 6.75 billion units of electricity have been fed to the state grids so far described by Rakesh Bakshi [2]. Several assessments studies worldwide have been thoroughly documented. Currently all the wind potential measurement techniques utilize the wind speed and wind directional data for at least a year to assess the wind resource over an area of interest. Usually, the wind data gathered is in the form of an hourly average formats which sufficiently take all the wind speeds and directional variations in consideration But the existing data collection and processing is not entirely applicable for the low windy locations. Similarly, many investigators have proposed simple expressions for vertical extrapolation of wind speeds. Hence, different expressions are used for extrapolation of wind profiles at higher elevations and various methods are employed for assessing the wind resource

availability for the region. In this current study the review of wind potential estimation techniques for power generation is done. Further the discussion is done for the application of nonlinear CFD models over the linear computational models for assessment of wind potential in complex terrains. II. METHODS FOR ASSESSMENT OF WIND AS POWER SOURCE Wind power has been acclaimed as one of the most potential and techno-economically viable renewable energy sources of generation. For securing maximum output of power, using a given type of wind electric generator, an assessment of the wind resource available at any prospective site is essential [3-6]. Table 1 the summarises of the various methods of wind potential estimation right from preliminary site assessment and field survey to the advanced wind speeds measurement and analysis techniques for prediction of wind resource of the site. In this table Javier Sanz Rodrigo [7] had categorised the methods which comprise of the acceptable global standards for wind resource analysis and prediction for a long time. TABLE 1 THE TRADITIONAL WIND ASSESSMENT PROCESS COMPLEMENTED WITH ADVANCED APPROACHES [7] Task

Traditional Approach

Tools

Advanced Approach

Site Prospecting

Cartographic survey + onsite evaluation

Measurement Campaign

Onsite reference mast as close as possible to the hub height and several additional shorter masts (in large sites) Correlation with nearby historical observations

Political and physical maps. European Wind Atlas. Met office statistics of nearby stations 40-80 m tall masts, equipped with cups and vanes

Regional wind atlas produced with a mesoscale model. Integration of other feasibility parameters in GIS database Velocity profile and Turbulence characterization using dedicated instruments

MeasureCorrelatePredict (MCP) methods

Onsite virtual met mast with historical and homogeneous wind time series

Long-term Extrapolation

Microscale horizontal extrapolation

Linear model, near-neutral conditions

Wind Atlas Methodology (WAsP)

Non-linear model, different stabilities, builtin forest model

Microscale vertical extrapolation

Define most likely wind shear based on lower measurements and experience

Linear model, near-neutral and/or Experience

Profile calibration based on remote sensing and CFD modelling

Copyright © 2014 IJWRE, All right reserved

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Wind Farm Design

Analytical wake modeling

IEC Classification (Vref)

Vref from limited (1-3 years) measurement periods

Wind farm design tools based on WAsP Extreme Value Analysis Methods IEC 61400-1

Built-in wake effects CFD model

Figure 1 (a) Griggs-Putnam index G for wind deformations (b) Asymmetrical redial cross section (compression ratio C) displaced by prevailing wind direction [11].

Onsite virtual met mast with historical homogeneous wind speed time series

The figure 2 shows the relationship of the deformation of the tree with the prevalent wind direction for coniferous pine trees and evergreen broadleaved trees. It is evident from the figure 2 that the tree trunk and the crown gets bent and develops in the direction opposite to the direction of the prevalent wind. As the time passes trees show the permanent deformation given by Deformation Ratio.

The energy available in the wind has cubic relationship with available wind speeds, so an understanding of the characteristics of the wind resource is critical for all aspects of wind-energy exploitation which ranges from the identification of suitable sites and predictions of the economic viability of wind-farm projects through to the design of wind turbines themselves as given by Shikha, et al [8]. The different ways of wind resource estimation at a site is given by Landberg.et al [9]. The methods followed for the estimation of the wind resource at any given site usually comprise of preliminary assessment done by catching the local folklores, studying the flagging and deformation of vegetation surrounding the prospective wind farm site. Further, the on-site measurement is done for the validation and analysis of raw data using statistical analysis tools including linear models WAsP and more complex CFD tools like WindSim. Various other global datasets like NCAR, CRU are accessed and GIS tools also help in bringing out the better picture of the local wind climate. The people saying are generally termed as Folklore which is one of the primitive methods to estimate the wind climate of any region. As the inhabitants of the particular place are living there for years, therefore it is taken as for granted that their knowledge of the surroundings and understanding of the local weather is good enough for the preliminary gathering of the data. Further, the wind climate at the place can also be assessed by observation of the vegetation and the biological indicators of the region under investigation. As shown in figure 1(a) the flagging and stem deformations of the coniferous pines in the high windy locations classifies the wind according to the Griggs-Putnam Index. This index ranges from 0-7 for low wind speeds of about 3 m/s to very high prevalent winds of 25m/s causing the tree to throw the tree towards ground. Similarly figure 1(b) shows the compression of the consecutive layers of the tree trunk developed over the time in the prevailing wind direction. The figure 1(b) shows the development of trunk layers in such a manner that successive layers are more densely packed in the direction of wind. The trees and shrubs as the source of wind speeds indicators are comprehensively described by E.W. Hewson.et al [10].

Figure 2 Deformation Ratio D calculated from taking photographs from the perpendicular direction of maximum asymmetry of (a) Coniferous Pine trees (b) Broadleaved trees [11].

Equation 1 gives the deformation relation of the conifer pine trees with the bending ratios which further are having the linear relationship with the prevailing wind speeds.

D

   0  45

,

1

 5 

(1)

Where, α = Angle formed by crown of the tree and tree trunk on the leeward side. β = Angle formed by crown of the tree and tree trunk on the windward side. γ = Angle of tree trunk bent away from vertical.

D

a   0, b 45

1

a 5 b

(2)

Where, a = distance between tree stem and crown perimeter on leeward side. b = distance between tree stem and crown perimeter on windward side. γ = degree of stem deflection from vertical. The tree deformed at their crown are then calibrated for the prevailing wind regimes as per the recorded data and following the Regression analysis the linear relationship is determined for that region. Therefore, this equation is then calibrated using at least one year winds data and we can estimate the wind climates for the area afterwards. Equation 3 gives the linear relationship between the deformation rations with the wind regimes present in particular place: (3) V  ax  b Where, a, b = constants x = the deformation ratio, D; Griggs-Putnam index, G, or the Barsch index, B. Compression Index C given by equation 4 was developed by the Wade and Hewson in 1979 gives the relationship of the compression of the tree rings along the opposite direction of prevailing winds and the wind regimes present at the site under study:


27


C

TL TW

(4)

Where, TL is the average width of tree ring along leeward side of tree pith. TW is the average width of tree ring along windward side of tree pith. A method often used in estimating the resource at a site is Measure–Correlate–Predict (MCP). The idea is that the resource at a potential site can be determined by using a short measuring campaign at the site and then correlating these measurements with an overlapping but climatologically representative time series. Climatologically representatives are obtained by measuring for at least 5 but preferably 10 years as given by Landberg, et al [9]. Matthew A. Lackner, et al [12] discussed round robin site assessment method while José A. Carta, et al [13] discussed the new Measure-Correlate-Predict (MCP) method given by figure 3 to estimate the long term wind speed characteristics at a potential wind energy conversion site. The analysis employs the matrix of coefficient of determination (R2) and the root relative squared error (RRSE). The capacity analysis of the model to estimate the long-term wind speed probability distribution function, the long-term wind power density probability distribution function and the long-term wind turbine power output probability distribution function at the candidate site are discussed. For some sites no suitable reference station is available. In such cases, only site data is used and longer on-site data sets are desirable [14]. Author compares the performance of four of the MCP approaches found in the literature, by using a common set of data from a variety of sites (complex terrain, coastal, offshore).

Figure 3 Graph showing the Measure-Correlate-Predict (MCP) technique between long term data (blue) and on-site measurement for one year data (Red) [15]

Global databases usually comprise of the long term information of the overall phenomenon which are climatologically specific as far as the global wind circulation is concerned. The spans of these databases usually range from 10 – 50 years. NCEP/NCAR (National Centres for Environmental Prediction/National Centre for Atmospheric Research) and ECMWF (European Centre for Medium-range Weather Forecasting) are some of the research agencies which are working from the past few years for building up these databases. Figure 4 shows the mean wind speeds predicted by Numerical weather prediction models by employing the NCEP/NCAR databases.

Figure 4 Mean wind speeds at 850 mbar for the years 1976-1995 from NCEP/NCAR reanalysis database [9].

Landberg, et al [9] describes the importance of the global climate databases in weather forecasting and climate prediction. These databases are having the global spanning and cover the vastness of the whole planet. These databases usually take into account the high level wind circulation patterns and as they are available for the large span of time, the stability of the wind profiles and climate as a whole are effectively reanalysed and stated with good accuracy. However, the application of such analysis to the local wind climate prediction bears low resolution output. Further, the wind climate near ground is undetermined by such high level wind profile databases Ramachandra, et al [16][17]. In several papers and field studies done in different countries of the world, wind atlas method of resource estimation was widely used. The wind data measurement for long term studies and building of climatologically suitable databases is continuously prepared throughout the world. Earlier studies done in Syria which are cited by A. AlMohamad, et al [18] discusses the annual energy production of 11976 million kWh for if 0.6 percent of land area lying in the southern Syria is allotted for wind farm installation. The assumption he considered in the study included 8 numbers of turbines per km2 with a diameter of 50 m, efficiency and energy loss equal to 0.25 each. A large number of countries including European Union, North African countries, Russia have prepared their wind atlases. The India has also made absolute contribution towards developing its own wind atlas with the help of KAMM and WAsP models [19]. The major parameters considered in development of Indian wind atlas included terrain roughness, obstacle effects and observed wind climate of the region under study. It involved the linear models which gave the results acceptable for vast domain of the land area. Data required are orography and land-use grid maps. The surface roughness is determined from the land-use. The efforts were also done by Elliot, et al [20] in building the wind atlas for Armenia highlighted the detailed wind resource maps and other information to facilitate the identification of prospective areas for use of wind energy technologies for utility-scale power generation and off-grid wind energy applications. Highresolution (1-km2) grids of wind power density at 50-m above ground were realized using different computer simulation and modelling. National Renewable Energy Laboratory (NREL) being the nodal agency drew the precise maps with the help of a computerized wind mapping system that uses Geographic Information System (GIS) software. The findings highlighted that the wind resource abundant areas in Armenia are generally


28


sited to be the top of the higher ridges or mountains or channelling effect of wind in locations such as mountain passes. The northern, eastern, and southern parts of Armenia were bestowed with excellent wind profiles. Some latest comparison studies were also done between WindSim CFD analysis computational model and WAsP linear computational model. The results obtained gave the clear picture of the precision and accuracy of CFD models over the linear models like WAsP. However, the CFD computational models being more relevant in the urban and high roughness domains, the WAsP stays as the de-facto standard for the wind energy industry. Figure 5 gives the methodology and hierarchy developed by the Riso Laboratories, Denmark in the formulation of the Wind Atlas Analysis and Application Program (WAsP) for assessment of regional wind climate in the mesoscale and micro scale level. Moreover, the wind resource assessment in the complex terrain needs the better grid resolution to include the turbulence and flow separation at the edges and sharp corners of the urban terrain. Alex Kalmikov, et al [21] discusses the application of the CFD tool in the study of wind profiles in the urban terrain and sharp urban obstacles like buildings. In his assessment the CFD simulation evaluated the potential of wind energy on the Massachusetts Institute of Technology in Cambridge campus. The on-site wind measurements and data from surrounding reference sites were included into the CFD model to obtain long term local observed wind climate. Takeshi Sugimura, et al [36] calculated the wind profiles in the complex terrain in the urban environment of Japan. The 3-D mesoscale metrological model MSSG-A is used to analyse the wind field of the Tappi

Wind energy technology has been developing gradually and steadily for quite a long time worldwide. Therefore several methods, processes and algorithms have been developed which have come as a handy tool for benefit of the wind farm developers and wind energy industry as a whole.

Figure 6: Overview of the Energy Prediction Process Hierarchy [23].

Figure 6 indicate general step by step process for determination of energy from the available wind resource at the particular place. Palma, et al [24] in the detailed comparison study between linear and non-linear models in wind resource assessment found that the results of the traditional WAsP models in evaluating the wind regimes for complex terrain can further be modified and refined by using the flow models for boundary layers and turbulent flows near edges, sharp roughness changes in the non-linear CFD wind potential estimation tools like WindSim, MeteoDyn and others for better simulation results for horizontal as well as vertical extrapolation of wind profiles at the site. III. . CASE STUDIES FOR WIND RESOURCE ASSESSMENT Figure 5 The Wind Atlas Methodology used in European Wind Atlas [22].

wind park. The results generated were in close proximity to the experimental data. The simulations were calculated with a spatial 5m grid using non-linear and three-dimensional compressible Navier-Stokes equation given by equation 5:

 V  1     V .V   p  2V      v .V   f  t  3 

(5)

Where  is the volume viscosity coefficient, also known as bulk viscosity. v

With the development of wind energy throughout the world, the core focus remained in reducing the intermittent behaviour and regulation of the system output to smoothen and streamline the generation aspects of wind turbines and associated power systems. This paper provides the review of the several case studies to enlighten about the past and recent developments in wind assessment technology globally. The literature mainly focuses on the wind potential estimation in various countries. To cite few: Mounir AKSAS, et al [25] assessed the solar and wind resources at Batna, Algeria in which the findings pointed towards the use of Wind-Solar hybrid systems, as the place was frequented by low wind speeds in the range of 4-5m/s in annual


29


hourly average. In 2008 the wind resource assessment study was conducted by James Jensen [26] which investigated the potential for Nunam Iqua, Alaska, USA. AWS Truewind mesoscale weather prediction tool was instrumental in estimating long term wind resource in that area. In conclusion to this monitoring effort, the feasibility of utility-scale wind energy generation was studied. Turkey holds the excellent wind resource in its coastal regions near Aegean Sea. B. Ozerdem, et al [27] during the investigation of wind potential in the campus of Izmir Institute of Technology, Turkey found that the average annual hourly winds in the region crosses 7.17 m/s at 10 m height and 8.35 m/s at 30 m height. This proves to be the excellent resource in the name of high wind energy potential. Turbulence intensity came to be very high in the northern sector at 10 m height above ground. WindPRO’ and ‘WAsP’ softwares were used in evaluating the recorded wind speed data to create mean wind speed map of the campus. This study augmented the effort for development of Turkish Wind Atlas. Chang, et al [28] described the characteristics of wind and wind turbine characteristics in Taiwan which was based on a long-term measured data source (1961–1999) of hourly mean wind speed collected at 25 meteorological stations across the country of Taiwan. For some regions of Taiwan the availability factor, efficiency of wind turbine and capacity factor for the entire year varied from 0.794 to 0.929, 0.246 to 0.290 and 0.450 and 0.642 respectively. The country of Taiwan came out to be blessed with the good wind resource and wind turbine characteristics values were within available limits. Similar, study were conducted in Greece by N. Vogiatzis, et al [29] which discussed the availability of sufficient wind potential in the wide area of Mikra–Thessaloniki in Greece for the operation of a pilot solar–wind assisted desalination unit. The pilot study concluded that the low wind speed values observed were not sufficient enough to smoothly run the conventional desalination plants. However, the study encouraged the exploitation of low wind speeds for the operation of solar–wind hybrid units independently for power generation. A. Keyhani, et al [30] in the research article published the report on the potential of wind as a power source for the Tehran, Iran. The work involved the statistical analysis of 3hour average long term wind data using Weibull distribution laying emphasis on the wind energy to support the grid for meeting the peak energy demand for the city of Tehran. However, the resource estimation underlined the fact that the present wind potential was best suited for off-grid applications like wind generators for local consumption, battery charging, and water pumping. Al-Mohamad, et al [31] gave the assessment description for the Syria in which it was proposed that the wind resource in the country was exploitable and it can meet the twice the demand for energy at present. The mean annual wind speeds analysed were in the range of 6-8m/s measured at 10m from ground level. The wind profiles at higher elevations could be much better from the prospective of wind farm development. This exercise gave a thorough economic analysis and evaluation for the feasibility of any such type of wind turbines and wind farms at windy sites. Wind resource assessment and energy generation estimation was done in Grenada by D. Weisser [32], Bangladesh by Md. Fazlur Rahman [33], Saudi Arabia by Naif

M. Al-Abbadi [34] which was further supported by the wind potential estimation of northern Saudi Arabia by Ahmet Z. Sahin, et al [35]. In Tunisia wind potential assessment was done by F. Ben Amara, et al [36]. In Kuwait wind resource study was done by W. Al-Nassar, et al [37], Madrid by Emilio Migoya, et al [38], Sicily by M. Cellura, et al [39], Nigeria by G.M. Ngala, et al [40], Egypt by A.S. Ahmed Shata, et al [41] and in Mexico by O.A. Jaramillo, et al [42]. The extensive wind resource assessment program was followed in the Turkey. Several regional studies were done which included Wind energy potential estimation and micro sitting on Izmir Institute of Technology Campus, Turkey by B. Ozerdem, et al [43]. Further, the Turkish study was also done in effect of terrain and topography on wind speeds by Zekai Sen [44] and comparison study of the probability distribution and wind power density by A.N. Celik [45]. Recent study was conducted for wind potential assessment in Malaysia by M.R. Islam, et al [46] which concluded that wind profiles were unsuitable for power generation. Lastly, the study of wind resource assessment and wind power potential in India was pioneered by Anna Mani [47] in which the author collaborated with the Indian Meteorological Department for survey of wind and solar resources throughout India. Moreover, Ramachandran, et al [48] gave stress on the development of wind power industry in India particularly in the state of Karnataka and Himachal region for reaching out the energy demand of the people and the industry. The paper suggested the use of remote sensing and GIS for mapping the energy resources for analysing the variability in relation to the spatial and seasonal aspects of renewable energy. It also addressed the need for inclusion of renewable energy to meet the regional demand for energy in such a way that will have least affect on the nature and environment in the Himalayan region. Gopalakrishnan, et al [49] discussed the performance of the 3kW Wind-Diesel hybrid system in which the satisfactory working in the WG/DG mix ratio of 20: 80, 50:50, 80 : 20 have been analysed and final calculations showed that ratio of 80 : 20 was good enough for various capacities of 2 kW, 5 kW, 10 kW and 20 kW systems. The author further investigated relation between wind data and performance of hybrid systems at Trivandrum and Nagercoil (South India) and a wind farm of 27 MW capacities with 120 wind generators in the range of 200 - 300 kW. Trivedi [50] laid major emphasis on the environmental factors which have been effecting the wind energy generation in the Gujarat, India. Due to the seasonal changes and adverse affect of monsoons on the electrical aspects, the power generation from wind farm got badly interrupted. Further, flash over, lighting and salinity in western coastal region of India pose a greater threat to wind energy installations. Therefore, author suggested the wind energy conversion systems must be kept under strong vigil throughout the year for reducing the unscheduled interchanges to gain at the financial aspects of the wind power industry. Nilesh Diwakar, et al [51][52] while using the WAsP, predicted the wind climate for the hilly terrain near Bhopal, Madhya Pradesh. The wind speeds analysed ranged from 3-6 m/s at 10 m and extrapolated wind profiles touched the range of 4-9 m/s at the elevation of 120 m above ground level with the mean wind energy density of about 200 W/m2 which classified as class 2. In the similar paper the same author studied the wind potential for complex terrain in which WAsP


30


modelling resulted in the Annual Energy Production in the area under observation from 1 MW WTG as 2.040 GWh at 70 m hub height. IV. THE STATISTICAL TOOLS FOR WIND ENERGY ESTIMATION The wind speed data are generally recorded as a time series format, the average for that span i.e. hourly average, daily average are taken for study of monthly and seasonal variation of wind speeds. Wind flow being variable in nature changes its direction and magnitude instantly. Therefore, various mathematical distribution methods and statistical analysis tools were developed to give the real time picture of the wind profile for the region under observation. The tools utilised in the study of joint wind distribution including the wind speeds and direction of wind flow for the instants was discussed by several authors. W. Weibull [53] in his pioneering work on statistical distribution gave the 2-parameter valued distribution in which the scale and shape of the graph varies as per the changes occur in the instant values of the scale parameter (λ) and shape parameter (k).Weibull Distribution is established method for calculation of wind speeds probability distribution function at any given site. Further, its results comprise of the time span for which any particular wind speeds were recorded so that future prediction of the wind climate for the particular region can be generated and annual wind turbine energy production can be estimated. Figure 7 shows the change in the pattern of the probability distributions for different values of scale parameter (λ) and shape parameter (k).The Weibull Probability Distribution is given by equation 6,  k  x  k 1  ( x /  ) k e    x0    f ( x;  , k )   , 0 x0   

The parameter evaluated were,

 

x 1  y slope

at

H=1, h=x

TABLE 2 METHODS FOR ESTIMATING WEIBULL PROBABILITY PLOT FUNCTION IN GRAPHICAL METHOD [54] Method

(Fxi)

i n 1

Mean Rank Median Rank

i  0.3 n  0.4

Symmetrical CDF

i  0.5 n

The summary of the basic formulae for the statistical quantities is given in the contents of table 2. Table 3 summarises the basic formulae for the statistical quantities incorporated in the wind resource assessment and analysis of available wind resource by evaluating mean wind speeds, probability distribution functions and associated Weibull parameters. TABLE 3 RELATIONSHIPS FOR CALCULATION OF WEIBULL PARAMETERS

Reference

Year

Relation Type

Weibull

1951

PDF

(6)

Equations

1  U  c 1   k  

x   e 1t x 1dt



Here, x = wind speeds (m/s), λ = scale parameter, k = shape parameter.

Jamil

1994

Gamma Approximation

 x  

Justus

Mohammad A. Al-Fawzan [54] discusses the graphical and analytical methods for estimating the Weibull distribution parameters. H ( x) 

 x

x 1

x

1 1     1   12x 228x 2 

 u2

Figure 7 Weibull probability density function with different values of scale (λ) and shape (k) parameter[53].

0

 2x x e 

1978

Analytical/Empirical

Lysen

1983

Empirical

Rohatg, Nelson

1994

Graphical: Log - Log Plot

   2   1    k   U  1  2 1    1  k       2

  k  u  U 

1.086

, c

c  0.433    0.568   k  U 

U 1   1   k  1 k



 3 U 3  U 3 pU dU  c 3 1    k 0



 3  1   k Ke  3   U  3  1  3   k



U3

 h( x)    

(7)

The relation is simplified in equation 8 as follows,

ln( x) 

1



ln H ( x)  ln 

(8)

Further, discussion focuses on hazard plotting technique in which cumulative hazard function is drawn on log paper. The cumulative hazard function is given by equation 7:


31


 x H ( x)   h( x)     



(7)

The relation is simplified in equation 8 as follows,

ln( x) 

1



ln H ( x)  ln 

(8)

The parameter evaluated were,

 

x 1  y slope

at

H=1, h=x

Gustavo Silva, et al [55], suggests the new way of Weibull parameter estimation, in which the Equivalent Energy Method is discussed. The results show that the energy content distribution narrows downs the accuracy and best fit curves are generated mainly for wind data sets with relatively high shape factor values. The basis of this approach lies on the development of equation between the energy density of the recorded wind speed and the results of the Weibull distribution for that site. The condition to be satisfied is that Weibull should have a good fits around the average and the values higher than the average. Further, this method give best results for the high windy regions of the world as the wind distribution pattern makes the shape factor for that region higher in value to give excellent results. Using the condition of energy content equivalence between the observed wind speeds and the Weibull distribution, the scale factor (c) written from the mean cube expression as given by equation 9: 1

 3   v3m   c   3  1      k 

moment, empirical, maximum likelihood, modified maximum likelihood and energy pattern factor method. By following the maximum likelihood method the equation 11 gives the relation for the shape factor (k) and relationship for the scale factor (c) is given by equation 12: n  n k    v ln v ln vi    i i   k   i 1 n  i 1 n   k vi    i 1

1 c  n

1

(11)

1

n

v i 1

k i

k  

(12)

The modified likelihood method is the more improved form of the above mentioned evaluation technique in which the shape factor (k) and scale factor (c) are calculated by equation 13 and 14 respectively: n  n k      v ln v f v ln vi  f vi   i i  i  k   i 1 n  i 1 f v  0   k   vi f vi    i 1 

1

(13)

1

(9)

The Weibull shape factor, k, can be estimated applying the least squares technique to the following expression shown by equation 10, 1 1      3  3    3  3         v  1   1   v  1   1      i i              k     k       1 1    v3m 3  v3m 3    n      e  Wvi  e   i 1     k

k

2

   n  2     vi  (10)  i1   

Where, th Wvi is the probability of having wind speeds for i bin, n is the number of bins of the wind speed histogram, th vi is the highest wind speed value of the for i bin, and 3 v m is the mean cube (observed). Tian Pau Chang [56] has compared several numerical methods for estimation of Weibull parameters with regards to the wind energy application. The review study with mathematical formulation for the Weibull parameters has been done using six numerical techniques. The maximum likelihood method had given best results among the other traditional numerical estimation techniques namely, graphical, the

n  1 k k   c v f v i i  f v  0 i 1 

(14)

The results show that, in simulation test of random variables, the graphical method’s performance in estimating Weibull parameters is the worst one, followed by the empirical and energy pattern factor methods, if data number is smaller. The performance for all the six methods is improved while data number becomes larger; the graphical method is even better than the empirical and energy pattern factor methods. Further, the author has tried to do the comparison analysis of the Weibull estimation methods by doing the Monte-Carlo simulations, Kolmogorov-Smirnov tests, parameter error, root mean square error and wind energy error. The below mention equation 15 gives the method for Kolmogorov-Smirnov simulation tests:

Q  maxF v  Ov

(15)

where F(v) and O(v) are the cumulative distribution functions for wind speed not exceeding v calculated by using specified Weibull parameters and by observed (or randomly generated) time-series data, respectively. At the confidence level of 95% following equation 16 gives the critical value of KolmogorovSmirnov tests:

Q95 

1.36

(16)

n


32


Where n is the number of data points. Moreover, greater the value for Q above the critical value, more difference will be in between the theoretical estimate and time series under observation with given confidence interval. The root mean square error test relies on equation 17 for the accurate and precise results:

1 2 RMSE     y i  y k    n i 1  n

1 2

(17)

Where yi gives the actual values at time stage i, yic are the values computed from correlation expression for the same stage, and n is the number of data. The best bin size (B) can be properly determined by the following empirical expression given by equation 18 discussed by Johnson Richard, et al [57]:

vmax (18) 3.3 ln n  1 Where, vmax is the maximum wind speed in data set and n is B

the data number. The theoretical wind energy density for a time interval T is given by below equation 19:

Ew 

1 3  3 c 1  T 2  k

(19)

 is the air density. Further, energy can also be estimated from time-series data using equation 20: Ea 

1 3 v T 2

(20)

v3 is the mean of cube of wind speeds. V. VERTICAL WIND PROFILES AND SHEAR COEFFICIENT Wind shear is the variation of wind speed with elevation from ground. As shown in the figure 8 there is the linear increase in the wind speeds initially with the higher elevation but the wind speeds comes to achieve the constant value as we approach towards the EKMAN layer or planetary boundary layer. This variation of wind speeds with the elevation above ground is characterised by the roughness index of the region.

mind that the terrain is flat. The others depend more on the numerical weather prediction models developed mainly for wind profiling of the area. Worldwide wind data measurement at the meteorological masts is usually done at 10 m heights from the ground. However, the wind turbines for utility MW scale production are installed at 80 m or above ground. Therefore the wind turbine manufacturers and designers need the wind speed analysis at the higher heights for their calculations of wind turbine power curve and thrust curve response at the site so as to predict their performance and annual energy generation. Mainly the wind turbine manufactures and wind farm designers utilise numerical wind prediction models directly with M-C-P correlation and they come in 68% of the total users. Further, the minor class relies on the power law index which requires the handsome amount of field experience and they constitute the 30% of total users [59]. As it is observed that the wind shear profiles vary in space and time at a particular place and also change seasonally. Directional distributions of wind shear are also studied. In the paper Ray, et al [60] concludes that using either of the power law or log law will result in approximate estimation rather than accurate analysis and prediction. The results discussed showed the difference in the assessment of wind shear using above traditional laws. The log law is based on principles of boundary layer flow and is given below as Equation 21, U z   U z r 

 ln     ln   

z z0 zr z0

       

(21)

Where z and zr are the target and reference heights, respectively. U(z) and U(zr) are the target and reference height wind speeds and zo is the surface roughness length. Table 4 gives the ready reference surface roughness values for different categories of terrain. TABLE 4 SURFACE ROUGHNESS (Z0) VALUES FOR VARIOUS TYPES OF TERRAIN [61]

Terrain Description

Roughness value 0.00001

Very smooth, ice or mud

Figure 8 Wind shear profiles shows the increase of wind speeds with the increase in height above ground and reach a steady value while approaching the EKMAN layer in range of 150-2000m of lower troposphere [58].

There are numerous methods for vertical extrapolation of wind speeds at higher heights, some of them need field experience e.g. using a power-law profile it should be kept in

Calm open sea

0.0002

Blown sea

0.0005

Snow surface

0.003

Lawn grass

0.008

Rough pasture

0.01

Fallow field

0.03

Crops

0.05

Few trees

0.1

Many trees, hedges, few buildings

0.25

Forest and woodlands

0.5


33


Suburbs

1.5

Centres of cities with tall buildings

3.0

The log law with some modification is introduced having concept of effective ground level at the given site. It is often used to account for the affect of tree canopies on wind shear. The modified log law is defined as Equation 22 [62],

U z  U z r 

 zd   ln   z0    z d   ln  r  z0 

SR 

U 90 U 60

Where

U 90 and U 60 are the measured wind speeds at 90m and

(24)

60m heights respectively. Table 6, summarises the different power correlations given by researchers relating to the function of several parameters governing wind profile at a place time to time. TABLE 6

(22)

Further, the log law does not satisfy the conditions for all places and regions. As the log law is mathematically undefined for time periods where the wind speeds at two different heights are the same. However, if wind speeds decrease with height, then the calculated surface roughness length for that time period is unrealistically large. For these reasons, wind speed data matching these criteria were excluded from log law calculations. Another popular model for wind shear is the power law. It is an empirically relationship developed mostly regarding the relatively flat terrain given by Equation 23. Here, α is the power law exponent. Researcher have used the oneseventh power law, where α = 1/7 for relatively flat terrain. Similarly, table 5 summarises the values of power law exponent for different description of terrain.

TYPICAL POWER LAW EXPONENTS USING DIFFERENT CORRELATIONS

Reference

Year

Correlation

Justus

1978

Function Velocity Height

Counihan

1975

of and

Power law exponent (α)





0.37  0.088ln U ref  Z ref 1  0.088ln   10

Function of Surface 2 Roughness (Z0)   0.096 log 10 Z 0  0.016log 10 Z 0   0.24 0.001 < Z0 < 10m

Spera

1994

Function of Surface Roughness (Z0) and Velocity

SR 

U 90 U 60

The comparative study finds out that generally accepted wind shear trends are not necessarily true. For example, earlier  z  U z    (23) research has shown that wind shear varies in the summer and in U zr  zr  the winter. However, for two of the forested sites, the wind shear was highest in the summer [63]. Wind shear is also usually assumed to be greater at sites TABLE 5 with the complex terrain, but interestingly, the flat site of TYPICAL POWER LAW EXPONENTS FOR VARYING TERRAIN Hatfield had significantly greater wind shear than Boulder, a site with fairly complex terrain. Furthermore, due to complex Terrain Description Power law terrain at Boulder, the lower height wind data in some of the exponent (α) direction sectors did not accurately represent the hub height Flat ground, water bodies like lake or 0.10 wind shear [60]. ocean Elizabeth Walls, et al [64] discusses the usage of SODAR in wind speeds measurements for the region of varying Small grass on untilled ground 0.14 roughness levels. In the paper author has taken the SODAR data recording for the forest covered areas, plain and flat areas Even rural side large grass, less trees 0.16 including ridge line and open fields. Further each and every Ripe harvest, hedges, tree groups 0.2. site had also the met tower placed for the time period of data Numerous trees and some settlements 0.22-0.24 collection. The anemometers were placed at 30m, 40m and 50m and at some locations the wind speed recording was also Areas under woods – small towns and 0.28-0.30 done at 45m and 60m. The results showed the good correlation suburbs coefficients between the mast readings and SODAR measurements in the range of 0.96-0.98. City and townships with 0.4 Moreover, the study conducted above showed the deviation tall buildings in the power exponent values when measured from meteorological mast and SODAR. There were percentage The author discusses the application of modern technology of difference in power exponent values for different locations as LIDAR and SODAR for the wind profiling at different heights provided in the table 7 and it was found that the estimate from as give by equation 24 for achieving the accurate analysis and the traditional mast data was prone to errors. Further, it was found that extrapolating wind speeds, based solely on tower future prediction of the wind climate for the given region. data can lead to either an under or over

  






   

34


TABLE 7 SUMMARY OF ESTIMATED POWER LAW EXPONENTS [64]

Site Cranberry Bog in MA Open Field in KS Ridgeline in WA Wind Farm in WA

Triton Alpha (SODAR) 0.392

Tower (Mast) 0.443

0.165 0.061 0.176

0.266 0.044 0.148

alpha

% Difference 13.0 61.2 -27.9 -15.9

estimation of wind speeds at higher heights. SODARs and other remote sensing devices has the advantage to measure wind speeds across the rotor diameter. This makes the wind shear exponent to be estimated based on data collected over the entire swept area. Thus the whole process becomes smooth and uncertainty involved is greatly reduced due to the extrapolation of wind profiles at higher elevations using age old methods and models. Francisco, et al [65] focuses on the major factors affecting the wind flow and wind regimes at any location. In the paper his observations of the wind speeds at different heights gave the reason for giving the proof that the wind is a time and space varying quantity. The stress laid on the wind shear is eminent in relation to the power generating potential of localised winds with the introduction of Monin-Obukhov method which is the most widely used to depict the wind speed v at height z by means of a log-linear profile clearly described by equation 25:

vf  z  z  v z       ln K  z0  L  vf

speed and that simulated wind speed using WindSim appears to be more accurate, i.e. closer to the measured wind speed than WAsP results. Alex Kalmikov, et al [21] in the paper focuses mainly on the advancement in the field of CFD analysis in relation to wind resource assessment. In the paper the wind potential estimation is done in the CFD simulations have been used to evaluate the wind energy potential on the campus of the Massachusetts Institute of Technology in Cambridge, MA. The met tower measurement data is also utilised to augment the wind potential assessment more accurately. The met towers (NRG, systems) were placed at two locations and wind speeds were recorded with 3 cup anemometers at 15, 20 and 26 meters and with weather vane sensors at 15 and 20 m. Other tower had four cup anemometers, two at 20 m and two at 34 m with a single vane sensor at each height. The 10 minute average values of wind speeds were recorded. The CFD model followed is UrbaWind by MeteoDyn, Inc. UrbaWind solves Reynolds Averaged Navier-Stokes equations (RANS), i.e. the averaged equations of fluid mass and momentum conservation, for steady incompressible flow given by 26 and equation 27:

 u i 0 x i

 (25)

Where, z is the height, is the friction velocity, K is the von Karman constant (normally assumed as 0.4), z0 is the surface roughness length, and L is a scale factor called the MoninObukhov length. The function ξ(z/L) is determined by the solar radiation at the site under survey. The equation 25 is valid for short periods of time, e.g. minutes and average wind speeds and not for monthly or annual average readings. This equation has proven satisfactory for detailed surveys at critical sites; however, such a method is difficult to use for general engineering studies. Thus the surveys must resort to simpler expressions and secure satisfactory results even when they are not theoretically accurate (Johnson, 2001). Görkem Teneler [66] in his thesis presents the wholesome analysis of the wind potential assessment in the Northern Sweden in which the major emphasis is laid on the comparative study of the linear mesoscale wind climate prediction models like WAsP and CFD based non-linear models like WindSim for the accurate wind potential estimation in complex terrain. The complex terrain is bestowed with the varying roughness lengths and different roughness values are encountered frequently. Therefore, the wind shear values also bound to change suddenly with respect to particular direction and location. The difference between measured and predicted wind shear values estimation by the linear model WAsP were in range of 10-20% while the CFD model WindSim estimated values were much lower in range of -8 to 0.43%. The above paper concluded that both softwares overestimate the wind



 u j ui x j

(26)

  P   xi

  u i uj         u i' u 'j   Fi  0 x j   x j xi  

(27)

The effect of porous obstacles is modelled by introducing a sink term in the cells lying inside the obstacle by introducing relation 28:

F  C DV U U

(28)

Where CD is a volumetric drag coefficient, which is proportional to the density of the porous obstacle, and V is the volume of the considered cell. Though CFD tools are wide spread in use now a days, still absolute values of the differences in wind power density and in mean wind speeds depend on the quality of the available data, resolution and accuracy of the computational techniques and are subject of further investigation. A. Llombart, et al [67] also laid stress on the accurateness and precise estimate of wind climate using CFD based mesoscale computational prediction model WindSim. In the paper it is concluded that terrain features do have the effect on the overall wind potential of the area. Thus using the CFD numerical prediction models, the care should be taken in drawn the high resolution terrain maps of the region under observation. Ebubekir Firtin, et al [68] raises the concern over the traditional numerical and statistical models used in the extrapolation of wind speeds at higher elevations by presenting the case study which mainly focuses on the percentage difference in the energy production. According to the author, at Balıkesir when the wind speeds were extrapolated vertically at hub height of using prediction models, the energy generated is 49.6% lesser than the energy prediction when data measurement was done at hub height itself. Thus, the study presented the excellent pilot study and stressed upon the fact of


35


measuring wind speeds at hub height for better AEP prediction. Further, the usage of extrapolation technique must be lessened to the extent and should be used as the last option or the site embarked with uneven or complex terrain. VI. CONCLUSION In the assessment of the wind power potential of a site, most investigators have been using simple wind-speed distributions, parameterized solely by the arithmetic mean of the wind speeds. Currently all the wind potential measurement techniques utilize the wind speed and wind directional data for at least a year to assess the wind resource over an area of interest. Usually, the wind data gathered is in the form of an hourly average formats which sufficiently take all the wind speeds and directional variations in consideration. But the existing data collection methods and way of processing the measured data is not entirely applicable for the low windy locations. In low windy regions observation of local vegetation development and its physical formation can also give the positive correlation with the wind flow pattern. Many investigators have proposed different equations and statistical models that are used for extrapolation of wind profiles at higher elevations above the ground. However latest methods viz SODAR & LIDAR employed for assessing the wind profiles at higher elevations in the region provide more accurate measurements. Various studies done worldwide concluded that CFD tools are more effective and precise for wind potential estimation in complex terrain. But linear simulation tool viz WAsP is still widely used in industry. ACKNOWLEDGMENT The support of the teaching faculty and staff of Centre for Energy and Environment is duly acknowledged. I am heartily thankful to the Professors for providing the necessary advice and guidance in the pursuit to meet the goals and objectives of this study successfully. Further, the selfless efforts of my seniors and colleagues are deeply appreciated.

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AUTHORS PROFILE: Lalit Anjum, born in Srinagar, Jammu and Kashmir, India on 2nd July 1987. The author earned first class Bachelors in Engineering (B.E.) degree in Instrumentation and Control from University of Pune, India during 2009. Author is alumni of National Institute of Technology, Hamirpur, Himachal Pradesh, India and has earned his Master’s in Technology (M.Tech) degree in Energy Technology during 2012 from above institute. Presently author’s field of interest lies in Renewable Energy technologies including Wind Resource Assessment, Solar Photovoltaic and others green technologies. Further, author has attended several trainings and workshops comprising of “STC measurement and testing of solar PV modules at Solar Energy Centre, India and calibration, testing and analysis of electronic and electrical instruments at National Research and Technology Consortium, Himachal Pradesh, India. He has also done the summer training in operation and maintenance of 200 kW solar PV power plants at Eternal University, India. He has also attended the 11th Wind Energy Technology Course at Centre for Wind Energy Technology; India.


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