An investigation of the impact of wind speed and turbulence on small wind turbine operation and fatigue loads

Renewable Energy 146 (2020) 87e98

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An investigation of the impact of wind speed and turbulence on small wind turbine operation and fatigue loads Anup KC a, *, Jonathan Whale a, Samuel P. Evans b, Philip D. Clausen b a b

School of Engineering and Information Technology, Murdoch University, Murdoch, WA, 6150, Australia School of Engineering, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW, 2308, Australia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 October 2018 Received in revised form 28 February 2019 Accepted 21 June 2019 Available online 24 June 2019

This paper investigates the operation and loading of a 5 kW HAWT using the aeroelastic code FAST. Wind € data from built environment site at Port Kennedy (PK) and from a flat terrain site in Ostergarnsholm (OG), are analysed and compared with IEC 61400-2. The longitudinal turbulence intensity (TIu) in the PK wind field was 22%; which was higher than the estimated value in IEC 61400-2 Normal Turbulence Model. The TI in the flat terrain (OG) was below 18% for all mean wind speeds. The selected wind conditions from the two locations were used as input in FAST simulation to investigate the performance and loading of the turbine. The elevated turbulence in PK wind fields increased the output rotor power which was more than that predicted by the standard. Similarly, PK wind field also showed higher blade root flapwise bending moment resulting into twice as much damage load on the turbine blades due to large short-term fluctuations in both wind speed and direction. This value for OG was below the standard's prediction. We observe that the current IEC standard seems inadequate for urban siting of SWTs and requires modification for more reliable deployment in turbulent sites. © 2019 Elsevier Ltd. All rights reserved.

Keywords: HAWT Built environment IEC61400-2 Turbulence Fatigue loading Damage equivalent load

1. Introduction The application of small wind turbines (SWTs) in built environment is relatively complicated compared to installing them in open terrain environments because turbines sited within urban or suburban areas are exposed to higher level of gust factor and turbulence compared to those installed in less rough terrain like offshore areas, or flat grasslands [1]. The wind conditions in the built environment are characterized by low mean wind speeds and increased level of turbulence due to high surface roughness, atmospheric instability, and interaction of the oncoming wind profile with surrounding obstructions. These features of urban wind flow fields challenge the safe and reliable deployment of SWTs within the built environment [2]. SWTs are designed based on the International Electrotechnical Commission (IEC) 61400-2 Design requirements for small wind turbines [3] which is based on open terrain wind conditions. The standard specifies design loads for SWTs and suggest the use of von Karman and Kaimal spectral density functions in turbulence models to simulate the wind fields, calculate the design loads and

* Corresponding author. E-mail address: [email protected] (A. KC). https://doi.org/10.1016/j.renene.2019.06.124 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

predict structural loading. SWTs designed according to the standard have not been adapted for the urban wind conditions and most of the SWTs installed in the built environment are sited with limited understanding of wind conditions of the candidate location and the influence of surrounding topography. The turbines are subject to the effect of inflow turbulence that may affect their output power performance, loading and fatigue and loading behaviour and safe operating lifetime. Recent studies in Refs. [4,5] have questioned the adequacy of the wind field model prescribed in the standard when applied to turbines operating in the built environment. The open-terrain wind model fails to incorporate all the wind dynamics related to such turbulent sites. IEC 61400-2 Annex [M] [3] does include extreme urban wind conditions as other wind conditions and advises that “the standard wind condition model is no longer valid for use by the designer without modification”, yet does not provide any suggestions as the modifications required to address urban wind conditions. The objective of this study is to identify how wind speeds and turbulence in urban wind fields deviate from the estimation of IEC 61400-2 standard and to access their impact on a turbine's power output and fatigue loads.

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2. Theory and methodology The estimation of turbulence strength in the time interval, Dt, is given by the turbulence intensity (I), as shown in Equation (1), which is a basic measure of the overall level of turbulence and how variable the wind flows. The IEC 61400-2 standard uses a Normal Turbulence Model (NTM) for normal wind conditions to describe turbulence and turbulence intensity (TI) and describes the relationship between longitudinal turbulence and wind speed. The standard defines a ‘characteristic turbulence intensity’ as the 90th percentile of the turbulence intensity measurements binned with respect to wind speed.

I¼

su

(1)

U

In the NTM, 10-min averaged winds are binned with respect to wind speed. Bin widths 1 of m/s are adopted and the mean value and the distribution of the standard deviations of longitudinal wind speed, su , are calculated for each wind speed bin. The characteristic value of the turbulence is given by the 90th percentile of turbulence intensity, obtained by Equation (2), assuming a Gaussian distribution for the turbulence values.

su;90pc ¼ s þ 1:28ss

(2)

where s is the mean value of the distribution of standard deviations of 10-min averaged longitudinal mean wind speed, and ss is the standard deviation of the su values within the bin. The expected deviation of the longitudinal wind speed as per the NTM in the standard is given by:

su;90pc ¼


I15 15 þ aU ða þ 1Þ

(3)

where I15 is the characteristic longitudinal turbulence intensity at U ¼ 15 m/s, ‘a’ is a dimensionless slope parameter, and U is the magnitude of the average of 10-min three-dimensional wind speeds at the hub-height of the turbine. Equation (3) was proposed by Stork et al. [6] and is based on open terrain wind data having hub-height wind speeds between 10 and 25 m/s. From IEC 61400-2, I15 and ‘a’ are 0.18 and 2, respectively, which reduces Equation (3) to:

su ¼ 0:9 þ 12U

(4)

Equation (4) can be rearranged in terms of longitudinal turbulence intensity, Iu , as:

Iu ¼

0:9 þ 0:12 U

(5)

IEC 61400-2 designates a maximum Iu of 18%, however many built environment installation sites have registered longitudinal turbulence intensity values well above this value, and this has been attributed by researchers to the high concentration of roughness elements in the area [7e9]. These studies have shown that wind fields in urban areas are more turbulent than in the rural areas/flat terrains. KC et al. [5]. compared a built environment wind condition with the IEC wind condition and affirmed that the urban wind flow field has 6% higher level of turbulence intensity than estimated by the NTM. To infer how appropriate the currently utilized von Karman and Kaimal spectral functions in the IEC standard are for the SWTs in urban settings, Tabrizi et al. [10] compared the spectra between the measured data and model predictions. The authors observed that both the standard spectral functions underestimated the measured spectra and suggested a corrected Kaimal spectral

function for better agreement with the measured values. Damage related to flapwise bending of wind turbine blades is a common type of failure experienced by SWTs. When turbine is aligned with the wind, blade flapping is either in the direction of the wind or against it. The largest stresses on the blades are due to flapwise bending and thrust forces on the blades are of particular importance. Edgewise (or lead-lag) motion is in the plane of rotation and is associated with fluctuations in torque on the rotor from the wind or in reaction torque on the rotor from the generator. In terms of magnitude, edgewise bending moments (EBMs) are less significant than the FBMs. The damage on the blade caused by FBMs is more severe than EBMs due to the low sectional stiffness of the blade and high aerodynamic moment. In turbulent wind conditions, gusts occur very quickly causing large instantaneous loads which are detrimental to the blades. When such gusts happen, the rotational speed of the turbine is unable to attune itself to the approaching wind and results in exceedingly large local angles of attack which, in turn, causes large drag forces and bending moments in the flow direction [11]. Assessments on fatigue loads of turbine components and power performance are a common practice for large wind turbines or wind farms using different numerical and experimental methods/ techniques. Mouzakis et al. [12] introduced an analytical method to identify a parameter for fatigue loading of wind turbines. Their proposed methodology showed that turbulence in wind was the main fatigue causing parameter for all wind turbine components. The fatigue loading in complex terrain due to turbulent wind was as high as 30% compared to flat terrain operation. Other authors state that, in addition to elevated turbulence, sudden change in wind direction and extreme wind conditions like hurricanes, storms, etc. can lead to serious fatigue loading on the turbines. Such events adversely affect the blade's aerodynamic behaviour, turbine's performance and furling limits. Dimitrov et al. [13] concluded that high turbulent intensity can be linked to the fatigue failure of the turbines and the accumulated fatigue damage also increases with the turbulence. More specifically, high longitudinal turbulence intensity can have a detrimental effect on blade aerodynamic performance mostly due to stalled conditions occurring when the angle of attack changes because of sudden change in wind speed [14]. Vasilis et al. [15]. claimed that the elevated turbulence intensity of the wind flow was primarily responsible in reducing turbine structure fatigue life on large wind turbines within wind farms. They studied the impact of complex terrain wind conditions on aeroelastic model of 500 kW machine using GAST (General Aerodynamic and Structural numerical Tool for wind turbines) to infer that the turbulence intensity exacerbated the fatigue damage of wind turbine blades significantly. Additionally, small lengthscales and strong-three dimensionality of the incoming wind flow field, which are common features of urban wind, are the secondary factors that are also no less significant in causing higher fatigue causing blade loads. Isamaiel et al. [16] studied the effect of turbulence intensity on the fatigue lifetime of 1.5 MW model wind turbine using FAST. The authors used both von Karman and Kaimal turbulence models at different turbulence intensities to estimate the fatigue life of the turbine blades and tower. They inferred that the increased turbulence also increased thee extreme loading on the turbine and resulted in higher damage equivalent loads. They too found out that that both the spectral model gave a closely matching results and was no significant difference in the results of fatigue behaviour of the turbine. For large wind turbines it is common to employ structural monitoring of the health of wind turbine components e.g. Bouzid [17]. Small wind turbine manufacturers, however, often operate on a very small budget and focus on simplicity and reliability. Thus incorporating such structural health monitoring techniques in

A. KC et al. / Renewable Energy 146 (2020) 87e98

small wind systems might not be economically feasible and can be technically complex. There is sparse experimental data on the fatigue loading of small wind turbines in general since instrumentation and monitoring of installed small wind turbines is typically excluded from the project budget due to the tight budgets of small wind turbine manufacturers. There is particularly little information on the fatigue loading of small wind turbines operating in the “dirty air” turbulence of built environments. In particular, the effect of elevated turbulence on the performance of SWTs deployed within such environments have not been comprehensively studied, however some studies have gather some preliminary results. Studies have also shown the elevated turbulence in the wind field has a major factor to reduce a turbine's fatigue life. Lubitz et al. [18]. concluded that higher turbulence in wind flow field boosts the power of the turbine within the operating range of the turbine while the increased turbulence at near-furling speed had negative impact on the power output. In previous work, the authors [2] compared the wind data at two urban locations with IEC standard and found that turbulence levels in those complex sites exceeds the estimate of IEC's NTM. The turbulence intensities at the turbine's design wind speed of 7.5 m/s were 34% and 29% at those two complex sites. They studied the impact of urban wind field on a SWT using FAST (Fatigue Aerodynamics Structures and Turbulence) and concluded that such turbulent winds imposed higher fatigue load on the turbine blades. When compared with the NTM, the turbulence intensities at both the sites were above 18% and the predicted damage equivalent loads (DEL) of the turbine blades for both the urban sites were higher than that estimated with the IEC Kaimal suggesting that a turbine operating in such an environment would experience a reduced fatigue life of the blades. The turbine would suffer more and had shorter working life operating in the built environment. KC et al. [5] also reiterated that the level of turbulence intensity in built environment wind field was higher due to higher variability in wind field and the turbine exhibited higher blade root flapwise bending moment which would render a higher damage load on turbine blades. They also observed that the increased turbulence in urban flow field enhanced the power output of the turbine. To address the gap in the literature, this study aims to provide a more comprehensive study on the impact of wind speed and turbulence on SWT performance, in terms of aerodynamic operation and performance of the turbine's rotor and mechanical loading on the rotor blades. This works builds on previous work by the authors [2] by looking at a much greater range of wind speed and turbulence scenarios and contrasting SWT performance and loading between a built environment and an open terrain site. The major contribution of this work is to provide insight that can aid in the upcoming revision of the small wind standard IEC61400-2 that will allow small wind manufacturers to design specifically for the installation of their product in built environment sites. International Energy Agency (IEA) Task 27 [19] has been documenting all the research and testing activities related to SWTs installed in built environment focussing on the IEA recommended practice guidelines for micro siting of SWTs and draft technical recommendations for IEC 61400-2. They have been collaborating with partner researchers to gain an insight on the characteristics of urban wind in terms of elevated turbulence and performance of SWTs within such wind conditions. Recently in their final meeting held in September 2018, they drafted a technical report and enlisted the results/inferences based on those studies done on urban wind conditions and have suggested recommendations on the needed technical changes to IEC 61400-2. This technical report references some of the authors' works on the characterization of urban wind fields and relevance of current IEC standard for urban installations of SWTs. This study utilizes the wind data obtained from wind

89

monitoring systems at two disparate locations, Port Kennedy (PK) € in Australia and Ostergarnsholm (OG) in Sweden, as shown in Fig. 1. Both the sites have SWT installations, although only PK had turbines installed during that period when wind observations were recorded. An ultrasonic anemometer was installed on the façade of the rooftop of a warehouse in Port Kennedy, 14.44 m above ground level. The warehouse is a rectangular building of height 8.5 m, with its long-axis oriented to NNE-SSW, and an almost-flat roof with a façade wall around the edge. The coast of the Indian Ocean is around 5 km away from the warehouse. There are light industrial, commercial and residential buildings as well as light vegetation around the periphery of the warehouse to make it a built envi€ ronment site. Ostergarnsholm is a small island in Gotland, Sweden, near the Baltic sea. It has a meteorological mast installed with ultrasonic anemometers at different heights. Wind data at 17 m height from the ground level are selected for this study. The € Ostergarnsholm data are undisturbed by the mast and other nearby objects in the sector 80 e315 . The datasets under the influence of tower shadow were removed from the OG data. The wind data from PK did not require any data filtering. As a practical approach to wind field characterization, the first two statistical moments of wind speed time series are considered. Wind datasets from the two sites are separately analysed using custom Matlab scripts to evaluate the first-order (U) and secondorder (su ) one-point statistical moments of the velocity time series. Tabrizi et al. [20]. showed that sampling wind data at 10 Hz and using a 10-min averaging period, Dt, gives upper estimates for the values of turbulence intensity and provides a conservative approach to ensure the wind resource assessment accurately captures the inflow so that the turbine can be designed to handle turbulent gusts. For this study, wind data was obtained from both sites, comprising of longitudinal, lateral and vertical wind components, sampled at 10 Hz for a period of six months are considered. The 10-min mean value of the magnitude of the three-dimensional wind speed, U ¼ uðtÞtenminute , is used together with the standard deviation, su , with respect to the same time interval Dt [21]. To compare the statistics of measured data with that of the NTM in the standard, the measured data required wind velocity measurements to be rotated from the anemometer's frame of reference to the reference frame of mean three-dimensional wind speed. The detail of this procedure is presented by Tabrizi et al. in Ref. [10]. For each 10-min wind record, the mean wind speed and direction along with their standard deviations in longitudinal, lateral and vertical directions were computed and Equation (2) & Equation (5) are applied to calculate the longitudinal turbulence intensity. 3. Aeroelastic modelling using FAST An aeroelastic model of a 5 kW Aerogenesis turbine is used to predict wind turbine loads and responses for the inlet wind conditions at PK and OG. This aeroelastic model of the turbine was developed in FAST v7.02.00 [22]. The predicted performance and loading of the turbine in open terrain (OG) and built environment wind (PK) conditions are compared and benchmarked against predictions using the current IEC Kaimal model to simulate inflow conditions. With a rated wind speed of 10.5 m/s and cut-in wind speed of 3.5 m/s, Aerogenesis machine is a two-bladed horizontal axis wind turbine (HAWT) installed on an octagonal monopole within the campus grounds of The University of Newcastle with a hub height of 18 m. The rated rotational speed (rpm) is 320 rpm, which yields a tip speed ratio (TSR) of l ¼ 8. The turbine was developed nominally as a Class III turbine with respect to the IEC design standard, which specifies an average hub-height wind speed of Uave ¼ 7.5 m/s, a design wind speed of Udesign ¼ 10.5 m/s, and a turbulence intensity of Iu ¼ 18%. This two-bladed turbine has a

90

A. KC et al. / Renewable Energy 146 (2020) 87e98

€ Fig. 1. Location of wind monitoring sites at (a) Port Kennedy (built environment) and (b) Ostergarnsholm (near shore flat terrain).

passive yaw control system using a delta-wing tail fin. The blades have a constant SD7062 airfoil profile along their span, a nominal length of 2.5 m and are constructed of glass fibre reinforced polymer (GFRP). Blade, tailfin and tower data are all used in the input to the FAST software, to model the structural aspects of the turbine and account for aerodynamic effects. Turbine dynamics related to yaw misalignment, tail fin effects, generator performance and tower motions are also included in the FAST model along with other key turbine parameters including rotor inertia, nacelle inertia, rotor overhang and tail fin boom length. The physical turbine has a self-excited induction generator (SEIG) and a maximum power point tracking (MPPT) variable speed controller that enables the drive shaft, and hence, the generator, to operate across a range of rotor speeds. This, in theory, enables optimum power extraction of the turbine by keeping the TSR close to, or constant, to its design value across the range of wind speeds. The physical turbine does not have blade pitch control but is provided with a braking mechanism to prevent the generator from going beyond the rated power. However, a braking mechanism, for the generator to limit the power output, is not included in the FAST model, and hence the output power of the turbine can reach beyond its design value at higher wind speeds. Full details of this publically available FAST model can be found in Ref. [23]. The development & experimental verification of this aeroelastic FAST model are in accordance with the methodology in Refs. [24,25]. The input wind speed datasets for FAST are prepared in TurbSim v2.00 [26] to simulate full-field wind times series. Ten-minute wind records, four typical and four non-typical, are chosen from each of the three wind speed bins, from both the sites, as indicated in Fig. 2. The values of the chosen mean wind speeds and their corresponding longitudinal standard deviation are presented in Table 2. From the wind speed bins of 4e5 m/s, 7e8 m/s and 10e11 m/s, the four typical wind records are selected from these records having a mean wind speed within the bin and a standard deviation lying on the 90th percentile fit line of the measured longitudinal turbulence. Similarly, four non-typical wind datasets are selected from those records with a mean wind speed within the selected wind speed bin lying above the 99th percentile fit line of the measured data. These non-typical wind cases have higher standard deviations in their respective bins. Thus, twenty-four wind sets are selected from each of the sites each and these 10min wind data are used in TurbSim to produce a full-field (FF) wind time series in format unique to TurbSim (*.bts) that serve as wind inputs into FAST. Such FF binary files are said to give maximum resolution in two-byte integers within a single file. These FF three-dimensional wind time series are generated from single point measured wind datasets from the two sites. Unique to

Fig. 2. 90th and 99th percentile fit of longitudinal turbulence fit for (a) PK and (b) OG measured data and IEC NTM.

it, TurbSim 2.0 has the capability to directly accept the wind time series data sets and generate binary full-field (FF) time series in a format designed to be read in FAST (coupled with AeroDyn). Additionally, to compare the measured statistics with that of the standard, three full field 10-min time series wind speed datasets are also produced with TurbSim using IEC Kaimal wind model at 4.5 m/s (at 32.0% TI), 7.5 m/s (at 24% TI) and 10.5 m/s (at 20.57% TI) to compare the measured statistics with that of the standard. The size of the grid and number of grid points are chosen as per TurbSim User's Guide to generate a full-field wind series for PK, OG and IEC wind conditions. A grid size of 12 m  12 m is chosen, which is large enough to encompass the rotor disk of Aerogenesis.

A. KC et al. / Renewable Energy 146 (2020) 87e98

For the choice of the number of grid points, a grid sensitivity test was run for the value of TI using IEC wind cases at 3 wind speeds and comparing the results with the corresponding value of TI from NTM. As shown in Table 1, the number of grid points are varied in each simulation in TurbSim to achieve the nearest value of TI for a given wind speed. For a grid size of 12 m  12 m, 19  19 grid points gave the best result for all three wind speeds and this mesh was then used to generate full-field wind data using a random seed number. All the chosen time series wind speed datasets are extrapolated to hub-height of the turbine using shear exponents € of a ¼ 0.3 for Port Kennedy and a ¼ 0.1 for Ostergarnsholm. Altogether, 51 simulations are undertaken to compare the performance and loading of turbine rotor for the measured and IEC standard inflow wind conditions. The performance of a wind turbine is generally characterized by how these parameters affect turbine power, rotor torque and rotor thrust. The output power of the turbine indicates the amount of wind energy captured, torque defines the size of the gearbox and the matching generator required and thrust influences the structural design of the tower. The 10-min statistics of these parameters are output from FAST at 10 Hz and post-processed in a custom Matlab script to evaluate and compare different performance and loading statistics of the turbine, through mean, maximum and ±1 standard deviation of the statistics for each inlet wind speed datasets in their respective wind speed bins. In this FAST study, to assess the structural integrity of the turbine rotor, one of the main parameters of interest to access the structural integrity is the blade response with respect to effect of different wind conditions. The damage equivalent load (DEL) on the turbine blade is calculated from the blade root flapwise bending moment using the rainflow counting (RFC) algorithm and Miner's sum method following the methodology discussed in IEC 61400.13- Measurement of mechanical loads [27]. This equivalent fatigue load would produce the same damage as the entire fatigue spectra would when applied at a rate of 1 Hz for the turbine design life of 20 years. The equivalent load, Req, is given by Equation (6).

P Req ¼

Rm i ni neq

m1 (6)

where i is the total number of rainflow counted load cycles, ni is the number of load levels Ri (equals to 1 for full cycle loads and 0.5 for half cycles) and neq is the number of 1 Hz cycles over a 20-year period. The coefficient m is the slope of the stress-cycle or €hler curve that plots the magnitude of a cyclic stress against the Wo fatigue life of the material. This coefficient is equal to 10 for GFRP blades.

91

4. Results: wind characterization Fig. 2 show plots of standard deviation of longitudinal wind speed versus mean hub-height wind speed at PK and OG. For each site, a linear fit to the 90th percentile values of the standard deviation distributions from each 10-min record is speeds compared with the IEC standard line, shown in Equation (4). The IEC standard overestimates the measured data from PK at lower wind speeds and underestimates the data at higher wind speeds (>4.5 m/s). For the flat terrain data of OG, the measured data are below the IEC standard for all the wind speeds. The longitudinal turbulence intensity at 15 m/s for PK is 22% and 10% for OG compared to 18% with IEC standard as seen Fig. 3, showing the value for the built environment site exceeds the IEC's NTM value. As evidenced in Fig. 2, there are few wind data beyond 12 m/s for PK while OG has a fairly good amount of 10-min wind sets until 20 m/s, which implies that OG has a wider operating range of wind speeds than PK. It is worthy to note that at the higher wind speed bins of 10e11 m/s, the number of 10-min data sets at PK are scarce and there are limited number of data sets lying above 99th percentile. Despite having relatively low wind speed wind data, the PK wind set has more 10-min records with higher standard deviations, which indicates that the wind field in such built environments are non-uniform and have higher variability in wind, both in terms of magnitude and direction. Such turbulent wind fields can have significant impact on both power performance and fatigue loads of turbine components. 5. Results and discussion: aerodynamic performance and fatigue loading The key parameters of interest from the FAST output are rotor torque, rotor thrust, blade flapwise bending moment and aerodynamic power of the rotor. FAST was configured to output values for these parameters at 10 Hz for a period of ten minutes. The maximum, mean and ±1 standard deviation values were calculated for these parameters for both the typical and non-typical wind speed datasets from PK and OG at all 3 wind speed bins, and compared with the FAST predictions based on the simulated inflow wind using the IEC Kaimal model. Fig. 4a compares the rotor power output of the turbine for the typical and non-typical wind conditions at PK, OG and IEC at three different wind speed bins of 4e5 m/s, 7e8 m/s and 10e11 m/s. Note that predicted mean rotor power exceeds the rated electrical power output of the Aerogenesis turbine (5 kW) at 10e11 m/s bin. This discrepancy is due to the limitation that the physical turbine

Table 1 Grid Sensitivity Test for simulated wind field in TurbSim. Wind speed (m/s)

Grid size (m)

Grid points ()

Simulated TIu (%)

TIu from NTM (%)

4.5

12  12 12  12 12  12 12  12 12  12 12  12 12  12 12  12 12  12 12  12 12  12 12  12 12  12 12  12 12  12

13  13 17  17 19  19 21  21 23  23 13  13 17  17 19  19 21  21 23  23 13  13 17  17 19  19 21  21 23  23

26.34 28.62 30.95 28.22 27.78 21.21 22.12 23.92 22.25 21.90 18.84 19.24 20.72 19.54 19.29

32.00

7.5

10.5

24.00

20.57

92

A. KC et al. / Renewable Energy 146 (2020) 87e98

Table 2 Selected mean values for typical and non-typical wind cases and their corresponding standard deviation from PK & OG wind data and IEC. (m/s)

Typical cases 4e5 m/s

Port Kennedy

€ Ostergarnsholm

IEC

Non-typical cases 7e8 m/s

10e11 m/s

4e5 m/s

7e8 m/s

10e11 m/s

U

su

U

su

U

su

U

su

U

su

U

su

4.09 4.09 4.24 4.40 4.01 4.22 4.41 4.53 4.50

1.38 1.37 1.43 1.49 0.56 0.59 0.59 0.63 1.46

7.10 7.20 7.39 7.39 7.14 7.31 7.72 7.79 7.50

2.05 2.09 2.21 2.09 0.95 0.98 1.03 1.03 1.80

10.25 10.37 10.38 10.46 10.53 10.55 10.60 10.64 10.50

2.64 2.65 2.74 2.78 1.33 1.33 1.34 1.39 2.15

4.04 4.47 4.58 4.69 4.09 4.40 4.68 4.84 e

2.69 3.23 2.75 2.66 2.12 1.09 1.12 1.31 e

7.27 7.31 7.35 7.42 7.02 7.25 7.50 7.83 e

2.74 2.77 2.47 2.51 1.56 1.62 1.32 1.36 e

9.94 10.08 10.19 10.19 10.32 10.52 10.80 10.97 e

2.96 3.19 3.02 3.14 1.66 1.52 1.74 1.86 e

Fig. 3. Longitudinal turbulence intensity (TIu) of PK and OG compared to IEC's NTM.

Fig. 4. (a) Mean aerodynamic power for the measured typical and non-typical cases compared with IEC standard and (b) Simulated longitudinal turbulence intensity values for all cases.

braking mechanism, which activates during the event when generation exceeds 5 kW, is not modelled in FAST. The figure shows that the PK typical wind cases have higher mean rotor power values than that from the IEC cases. The mean rotor power is 17%, 25% and 19% higher than the IEC wind cases for the respective bins. With typical OG wind cases, the mean rotor power is comparable to the IEC values of 514 W, 2372 W and 6240 W at all the three wind speed bins. The mean rotor power with the PK non-typical cases are

higher than the IEC values by 122% at 4e5 m/s, 30% at 7e8 m/s and 10% at 10e11 m/s bin which is manifested through the higher value of the turbulence intensities (refer Fig. 4b) for these wind cases. In general, the higher value of the mean rotor power for both the sites are due to the higher fluctuations in wind speed resulting in higher turbulence intensity. As the values of turbulence intensities at PK are fairly higher than those at IEC and OG, the power produced by the PK wind sets are also essentially higher.

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Table 2 has the value of mean wind speed and their corresponding standard deviations of the chosen wind cases from both the sites. The effect of increased turbulence intensity (due to higher standard deviation) on power output is apparent particularly with the non-typical cases as the non-typical cases produced more rotor power than the typical cases at the respective bins. It appears that the increased power output is a function of both mean wind speed and turbulence intensity. For PK non-typical cases at 4e5 m/s and 7e8 m/s bins, the output power is higher due to higher values of both mean wind speed and turbulence intensity. The non-typical cases at these two bins clearly have higher TI than the typical cases. However, it is interesting to note that at 10e11 m/s bin, the non-typical PK wind cases produced less power than the typical cases although they have higher standard deviation and thus higher turbulence intensity (refer Table 2). Although the nontypical cases at 10e11 m/s bin have higher TI, they are not able to boost rotor power as they do in other two lower bins because of the cubic relationship that rotor power has with mean wind speed. For 10e11 m/s PK bin, all the non-typical cases have lower mean wind speed than the typical cases. The increased turbulence with nontypical cases could not influence enough to exceed the power produced by the typical cases due to lower mean wind speeds. It seems if the turbulence intensity is reasonably close, then the dominating factor in terms of power is wind speed, and with increasing wind speed, there might be negative effect of elevated turbulence on rotor power. The trends from Fig. 4a and b suggest that increased turbulence in the wind field increases the aerodynamic power of the turbine. This result supports the findings of Lubitz et al. [18]. where the authors infer that increased turbulence intensity enhances turbine's power output at lower wind speeds. The authors also concluded that increased turbulence in wind at near-furling wind speeds have a negative effect on the power. As all the selected wind cases have mean wind speed within the operating range of Aerogenesis, the effect of increased turbulence on turbine's performance at near furling speed could not be evaluated in this study. Although the elevated turbulence intensity at PK (and OG) does appear to increase the aerodynamic power of the turbine up to rated power, it is expected that, after the rated power, it would be difficult for the physical turbine's control system to maintain the output power within such complex inlet wind conditions. A SWT operating in a turbulent wind field with large fluctuations, such as at PK, demands a robust control system to maintain the power output outside the design TSR value. The mean TSR at three wind speed bins for the PK, OG and IEC Kaimal wind cases are shown in Fig. 5a. For the wind speed bins of 7e8 m/s, the turbine is close to its design TSR of l ¼ 8 for typical wind cases from PK while the non-typical cases have slightly higher (~6%) TSR. For OG, the TSR is slightly lower than the design value for both typical and non-typical cases which is similar to the TSR value of l ¼ 7.8 from the IEC wind case. At 10e11 m/s bin, the IEC wind case has a TSR of l ¼ 8.4 which is slightly higher than the design value. All OG wind cases have similar TSR at this wind speed bin whereas the PK wind cases resulted in 19% higher TSR compared to the design value. In both the bins, the OG wind cases have the TSR comparable to that of IEC while PK wind cases have slightly higher TSR than the design value, perhaps indicating that a better control system would require for the turbines operating in such complex site to cope with the wind conditions outside the design TSR values. Having to operate at the higher TSR means that the turbulent wind in PK also puts the turbine blades under increased fatigue loads. At lower wind speed bin of 4e5 m/s, the TSR are non-uniform and spread out, particularly for the PK non-typical cases. Fig. 5b illustrates the effect of high fluctuations in wind on the rotor speed of the turbine. It compares the longitudinal wind speed and

93

rotational speed of the 1st typical and 1st non-typical data point in the 4e5 m/s wind speed bin corresponding to l ¼ 11.3 and 11.9 respectively. The typical wind case has more uniform wind time history both in magnitude and direction, there is not much variation in the corresponding rotational speed of the turbine. The nontypical case on the other hand has frequently varying wind in magnitude and direction. Despite having such variability, the nontypical case has maintained a similar TSR to that of the typical case. It is due to these larger fluctuations in wind speed and direction that it, along with other non-typical cases, exhibit higher value of tip speed ratio. With increase in wind velocity, the turbine gradually tries to adapt to the new wind speed. A drop in wind speed, however, does not cause the turbine to drop in rpm immediately due to the rotational inertia of the blades. Although high in variations, the changes in wind speed and direction are not instantaneous and thus a higher value of tip speed ratio is observed. A higher variation in TSR can be observed with PK's 2nd non-typical case exhibiting the lowest TSR (l ¼ 7.44) for the 4e5 m/s bin. Note that this wind set has the highest longitudinal standard deviation (Refer Table 2) and thus is likely to contain larger fluctuations in both wind speed and direction. It is possible that due to a sudden change in wind speed and direction, the rotor is not able to track the oncoming wind, leading to yaw misalignment at lower rotational speeds that yield a low average TSR. Given the limited number of 10-min records studied, it is difficult to draw firm conclusions about the performance of the turbine based on Fig. 5a. It is likely, however, the larger the fluctuations in wind speed and direction in the turbine inflow, the greater is the difficulty that the turbine's control system faces in maintaining the design TSR. A SWT operating in complex terrain where there is highly turbulent flow will demand a more robust control system to operate and keep TSR within the range of the design value. Highly turbulent flow fields have a high stochastic variation in mean wind speed and direction which may induce unsteady aerodynamic effects and high instantaneous structural loading on the turbine components. The FAST model of the Aerogenesis turbine has yaw behaviour resulting from the delta wing tail fin. With the observed fluctuation in wind direction, it is more likely that the turbine will be yawing more at PK to track the wind. When the wind direction changes rapidlye more than 180 change over ten minutese several instances with yaw misalignment can transpire leading to gyroscopic loading on the blades which might manifest higher loads due to such fluctuations not only in magnitude but also in wind direction. With higher damage load cycles, a turbine at PK is likely to suffer higher fatigue damage and have a shorter working life than the same turbine when operated at OG. Fig. 6 and Fig. 7 show mean and maximum values of the rotor torque and blade root FBM with ±1 standard deviation. The typical and non-typical data from both the sites have been plotted separately for clarity. For the typical datasets, the relationship between mean rotor torque and wind speed as observed in Fig. 6 is largely consistent among PK, OG and IEC wind cases. In average, there is a minor increase in the mean rotor torque for PK wind sets, indicating the turbine controller performed well in optimizing torque load. The maximum rotor torque is also fairly consistent at each wind speed bin except for the decreased value of the maximum rotor torque observed at 4e5 m/s bin for both the sites due to lower wind speeds. At higher wind speeds, the rotor torque is mostly identical, at 200 Nm for both the sites. When considering ± standard deviation range, higher variations in torque and hence power are observed in PK wind sets for both typical and non-typical cases. From Fig. 7, there is a maximum of 5%e21% increment in mean blade root FBM for the PK wind sets compared to the benchmarked IEC values, for both typical and non-typical data at all 3 wind speed

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Fig. 5. (a) Mean TSR for PK, OG and IEC wind cases and (b) wind speed and rpm time series plot for a chosen PK typical and non-typical cases.

bins. The typical OG cases have mean FBM values 6% higher than IEC values while the non-typical cases result in 8%e14% higher mean FBM values. There is a moderate increase of 1.2%e12% in maximum blade root FBM values than the IEC value due to typical wind cases at PK, however, the maximum values of blade root FBM with non-typical cases are significantly higher than the maximum values from the IEC wind. These values are 65%, 29% and 53% higher than the IEC wind at the three bins. The maximum FBM values with OG wind cases are lower than the IEC values for both typical and non-typical cases at all wind speed bins, as seen in Fig. 7. It is also evident from the figure that higher variations (±1 standard deviation range) in blade root FBM is seen for the PK wind data compared to the OG data, for both typical and non-typical wind cases, particularly at 7e8 m/s and 10e11 m/s bins. When comparing typical and non-typical values, it appears that there is greater impact of the larger wind fluctuations in the non-typical data on blade root FBM which is manifested as significantly higher blade loads. This significant increase in blade loads is more likely to have consequence on the fatigue life of the blade. A separate calculation from the FAST output shows that the edgewise bending moments are 6e15 times smaller than the flapwise loads at the given wind speed bins. So, the blade root

flapwise bending moment (FBM) is considered to dominate the loading behaviour of the turbine blades. The loads on the turbine blades are characterized in terms of fatigue damage from the blade root FBM imposed by the 51 chosen wind cases for the three different operating wind speed bins. For each wind case, the rainflow counting method, discussed in Section 3, calculates the fatigue causing damage cycles from the FAST time series of blade root FBM and converts it to a single equivalent load that would induce the same damage if applied for a period of 20 years’ operation at a frequency of occurrence of 1 Hz. The fatigue causing DELs for the blade-root flapwise bending moment for the different wind speed bins and inflow wind cases are shown in Fig. 8. Fig. 8 shows that the PK typical cases have a modestly higher damage values than the IEC valuesd an increase of 3%e23% with increasing wind speeds. The non-typical cases at PK produced higher DEL than the IEC at all wind speed bins, ranging from 14% to 105% increase; with the maximum of 115 Nm, 150 Nm and 254 Nm at the three wind speed bins of 4e5 m/s, 7e8 m/s and 10e11 m/s respectively. This is 105%, 32% and 54% higher than the IEC values at the respective bins, as summarized in Table 3. The higher the wind speed, the greater is the damage loads experienced by the turbine blade. The resulting high value of DEL produced by all the non-

A. KC et al. / Renewable Energy 146 (2020) 87e98

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Fig. 6. Mean and maximum rotor torque for PK, OG and IEC wind cases.

Fig. 7. Mean and max blade root flapwise bending moment for PK, OG and IEC Kaimal at 3 wind speed bins.

typical cases at PK are due to higher turbulence intensity and high standard deviation, meaning the larger variations in both wind speed magnitude and direction. Conversely, the DEL values with all the wind cases for the OG typical cases are below the IEC values. At OG, the non-typical cases clearly produced higher DEL than the typical cases, yet they were below the IEC Kaimal values. The combination of higher value of longitudinal turbulence intensity and high mean wind speed results in higher DEL at respective bins. Having said so, it must be noted that the DEL values are more sensitive to turbulence intensity at higher wind speed

bins and are susceptible to even a small difference at increasing wind speed. This can be well observed from Fig. 3 where large variation in TI can be seen at 4e5 m/s bin yet the influence in output power as well as DEL is moderate. Toward the higher bins, the difference in TI is small yet there is significant deviation in both DEL and mean rotor power (Refer Figs. 4 and 8). All the selected wind cases in PK, both typical and non-typical, have higher turbulence intensities than those of OG and hence, the PK wind cases are able to produce 14%e63% more rotor power and up to 2.7 times the DEL produced by the OG wind cases.

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Fig. 8. Damage equivalent load with typical and non-typical cases from PK and OG compared with IEC at three wind speed bins.

Table 3 Maximum DEL values for typical and non-typical cases at PK and OG compared with values from IEC at three wind speed bins. Wind speed bins

DEL (Nm) IEC Kaimal

4e5 m/s

56

7e8 m/s

114

10e11 m/s

165

a

Typical Non-typical Typical Non-typical Typical Non-typical

PKa

OGa

52 115 127 150 203 254

20 42 58 73 89 110

Max DEL values among four 10-min windsets

It is evident that the increased turbulence indeed plays a crucial role in enhancing the power performance but at the same time, imposes higher damage loads on turbine blades and hence reduces fatigue life. The chosen wind cases reveal that the wind conditions in complex sites like Port Kennedy are subject to high turbulence levels in excess to the standard. The performance and the structural integrity of the turbine deviates away from the estimate of the standard as such turbulent wind conditions are capable of derating the turbine's design life and can exhibit non-uniform power performance. Amidst the difficulty in predicting the actual operation of the turbine in such complex terrains, wind turbine manufacturers may opt to determine design loads either from aeroelastic simulations or risk overdesigning the turbine based on simplified load model (SLM) [4], both the methods being not always viable commercially and technically. It is desirable to have a revised standard that can incorporate urban wind dynamics and help make accurate estimates for turbulent sites instead of undertaking new monitoring techniques or experimental measurement campaigns. Looking into the time series of the wind datasets for the two chosen non-typical cases in Fig. 9, PK non-typical wind sets seem to have larger short-term fluctuations of wind speeds compared to that in OG, for both 7e8 and 10e11 m/s bins. The PK wind sets at both the bins have many instances of constantly changing magnitude and direction. The higher values of FBM and DEL in PK wind sets could be influenced by the observed short-term fluctuations in PK wind sets, which also indicate the time-scale of turbulence in the wind. It is expected at PK that there are many obstacles to break down the atmospheric turbulence into smaller eddies. The smaller size of these eddies compared to those at OG may explain the higher FBM and DEL since such smaller eddies are more likely to occur at PK that are on the same scale of the blade chord and blade length inducing dynamic loads on the blade. It is apparent that the higher turbulence intensity in wind field

is the due result of higher variability in the wind. Such wind fields, having higher and more frequent fluctuations in both magnitude and direction, results in higher level of turbulence and this is evident at Port Kennedy. While the literature has linked the higher fatigue loads experienced by the turbine/components with increased turbulence levels in the wind field, this investigation additionally infers that it is more apposite to relate the higher damage loads with higher wind fluctuations rather than turbulence intensity. Fig. 10 compares the DEL against longitudinal turbulence intensity (TIu ) and su . As discussed previously, the DELs appear more sensitive to turbulence intensity at higher wind speeds where even a small difference can result in larger impact on power and damage loads. This change at lower wind speed bin, however, is not as much significant at both the sites. From Fig. 10a, it can be ascertained that increasing level of turbulence intensity does impact the fatigue loads of the turbine blades, however, Fig. 10b has a clearer trend of higher damage load caused by the wind sets having higher standard deviations and the impact being more intense with increasing wind speed. It cannot be denied that increased turbulence level stems from larger fluctuations in wind field and such wind sets with high su intrinsically contain elevated level of turbulence. So, it is more rational in correlating the DEL with the standard deviation of the wind field i.e. the higher variability in wind flows, especially in terms of variability in longitudinal wind speed. su can be a more appropriate parameter than TIu to relate wind speed, wind turbulence and damage loads on turbine components. 6. Conclusion In this study, we investigated the high-resolution measured wind datasets from two contrasting locations and compared them with the wind model (NTM) in the current IEC 61400-2 standard. We found that the normal turbulence model used in current standard applied for the design of small wind turbines was inadequate with the urban wind conditions. The 90th percentile fit of measured data showed that the standard overestimated for wind speed below 4 m/s and underestimated for higher wind speeds for the urban site- Port Kennedy. Moreover, Port Kennedy wind had turbulence intensity of 22% which is 4% higher than the NTM € estimation. The level of turbulence with Ostergarnsholm wind field was below the NTM value for all wind speeds. As for the predicted performance of the turbine using aeroelastic code FAST, Port Kennedy wind field with both typical and non-typical inflowing wind fields increased the rotor power. Compared to the IEC wind cases, increase of 17%, 25% and 19% in rotor power were observed for typical wind cases while the nontypical cases produced 122%, 30% and 10% increase at the three chosen wind speed bins respectively. The first and the second order one-point statistical moments of velocity time series of su and TIu are the single metrics to describe the turbulence for a 10-min record. They appear to correlate well with the aerodynamic power where the chosen 10-min records having higher standard deviations and higher turbulence levels resulted in increased rotor power. However, there was no clear relation between su and TIu on the rotor torque, which remained largely consistent for both Port € Kennedy and Ostergarnsholm sites and amongst the different typical and non-typical wind cases. Because we have only considered the longitudinal component of the wind velocity while the rotor toque is more influenced by lateral component of the velocity in lead-lag direction, it may be interesting for future work to look into the impact of transversal velocity on rotor torque. The higher the wind speed, the more was the rotor power as well as the damage loads for both the sites. The predicted damage equivalent load indicated that the design life of the turbine's blade

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Fig. 9. Time series of longitudinal wind component of two selected 10-min records for PK and OG at 7e8 m/s and 10e11 m/s bin.

Fig. 10. DEL with PK OG and IEC wind sets plotted against respective (a) longitudinal turbulence intensities and (b) longitudinal standard deviations; note the sizes of the scatter circles vary with increasing wind speed.

at PK was shorter compared to IEC and OG. With the typical cases, Port Kennedy wind sets showed a maximum of 24% increase in loading (at 10e11 m/s bin); the blade damage loads with nontypical wind sets were 105%, 32% and 54% higher than that estimated by the IEC at the three wind speed bins. These values were lower than the IEC predictions for all the wind cases at € Ostergarnsholm. The higher values of su and TIu also correlated with FBM and DEL where the wind cases with higher turbulence intensity and higher variability at Port Kennedy ended up imposing higher flapwise bending moment and hence high equivalent fatigue loads. Moreover, it appears more rational to relate DEL in terms of su than turbulence intensity. When it comes to interpreting the wind field, it becomes imperative into look at the time series of inflowing wind which shows the importance of shorter fluctuations in wind speed and direction on FBM and DEL. The intermittent statistics associated with turbulent flow field can be interpreted with higher order statistical analyses in terms of incremental pdf's which will be dealt separately in future work. The discussed rotor loads show that the wind conditions at OG nicely conforms to the IEC standard and the turbine operating at OG will not have notable issues related to power and fatigue loads unlike PK where the turbine is expected to suffer much and have unpredictable operation due to prevailing complex wind dynamics. With higher longitudinal turbulence and DEL in the built environment compared to the values estimated by

the IEC 61400-2 standard, the wind model assumed in this standard for small wind turbines appears inadequate for the turbines to be sited in the built environment and the standard requires improvement to make it more inclusive of urban wind fields so that accurate prediction on turbine operation and loading can be done to guarantee its safety and performance. Acknowledgements This study is a part of the author's Ph.D. research work. The author would like to thank Murdoch University for awarding MIPS Scholarship for the duration of his Ph.D. candidature. References [1] A. KC, J. Whale, T. Urmee, Urban wind conditions and small wind turbines in the built environment: a review, Renew. Energy 131 (2019) 268e283. [2] S.P. Evans, A. KC, D.R. Bradney, T. Urmee, J. Whale, P.D. Clausen, The Suitability of the IEC 61400-2 Wind Model for Small Wind Turbines Operating in the Built Environment, World Renewable Energy Congress XVI, Murdoch University, Australia, 2016. [3] IEC 61400-2, Wind Turbines: Design Requirements for Small Wind Turbines, Australia Standard, Australia, 2013. [4] S.P. Evans, D.R. Bradney, P.D. Clausen, Assessing the IEC simplified fatigue load equations for small wind turbine blades: how simple is too simple? Renew. Energy 127 (2018) 24e31. [5] A. KC, J. Whale, T. Urmee, J. Peinke, M. Wacher, A comparative analysis of built environment and open terrain wind data by higher order statistics and

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