The paper presents the results of statistical load analyses based on data measured at the 5MW AREVA Wind M5000 onshore prototype. Measurements with standard meteorological measurement devices are analysed and compared to measurements with a pulsed LIDAR system which is enhanced with a multi-purpose scanning device installed on the top of the nacelle of the turbine. Based on these measurements statistical summaries of relevant meteorological parameters have been used for normative procedures to calculate the mechanical loads which occur at the wind energy turbine. It could be verified that LIDAR systems can substitute standard measurement devices for a load estimation of wind energy turbines

Bischoff, Oliver

Hofsäß, Martin

Rettenmeier, Andreas

Schlipf, David

Siegmeier, Björn

Online Publikationen der Universität Stuttgart

Category: LIDAR 
STATISTICAL LOAD ESTIMATION USING A NACELLE-BASED LIDAR SYSTEM 
O. Bischoff 1, M. Hofsäß 1 , A. Rettenmeier 1, D. Schlipf 1, B. Siegmeier 2 
1 Endowed Chair of Wind Energy (SWE), University of Stuttgart, Germany, +49 711/6856-8213,  
bischoff@ifb.uni-stuttgart.de; 
2 AREVA Wind GmbH, Bremerhaven, bjoern.siegmeier@areva.com 
 
 
Summary 
The paper presents the results of statistical load analyses based on data measured at the 5MW AREVA Wind 
M5000 onshore prototype. Measurements with standard meteorological measurement devices are analysed and 
compared to measurements with a pulsed LIDAR system which is enhanced with a multi-purpose scanning 
device installed on the top of the nacelle of the turbine. Based on these measurements statistical summaries of 
relevant meteorological parameters have been used for normative procedures to calculate the mechanical loads 
which occur at the wind energy turbine. It could be verified that LIDAR systems can substitute standard 
measurement devices for a load estimation of wind energy turbines. 
 
1 Introduction 
An accurate estimation of mechanical loads of a 
wind energy turbine is one of the key elements in 
the certification process for wind energy turbines. 
Usually the necessary data for the calculation of 
mechanical loads according to [1] is collected with 
anemometers installed at a met mast in the vicinity 
of the turbine in a distance of 2-4 times the rotor 
diameter D. The main disadvantage of such devices 
is the lack of information about the entire incoming 
wind field. The purpose of this work is to investigate 
and develop strategies to improve the statistical 
load evaluations by not only regarding point 
measurements of meteorological parameters with 
standard measurement devices of the incoming 
wind but by introducing the whole incoming wind 
field into the analysis. This is important because of 
the inhomogeneous character of the wind field in 
front of a wind turbine. 
Remote sensing techniques such as LIDAR offer 
such measurement possibilities of the wind 
conditions and are in addition cost and time-saving 
as it is not necessary to erect a met mast. This 
becomes even more important for offshore 
purposes. Such devices can be ground- or nacelle-
based but need different preprocessing routines to 
get verified data sets. 
This paper shows an approach to calculate the 
statistical data for the mean wind speed and 
turbulence intensity by means of data collected with 
a nacelle-based LIDAR system and a comparison of 
this data with standard measurements, as well as 
the exemplary calculation of load spectra and 
damage equivalent loads for selected components 
of wind energy turbines. 
This work is carried out within work package B.3 of 
the joint research project ”Development of LIDAR 
wind sensing for the German offshore test site”. 
Figure 1 illustrates the described process of a load 
estimation using a nacelle based LIDAR device 
enhanced with a scanning interface. 
 
 
Figure 1:  Outline of data processing 
2 Data Analysis & Measurement Results 
2.1 Experimental Setup 
The experimental data analysed in this research has 
been collected with a pulsed LIDAR system 
equipped with a scanning device developed by the 
Endowed Chair of Wind Energy (SWE). The whole 
system is mounted on the nacelle of a 5MW wind 
energy turbine with a rotor diameter D of 116 m and 
a hub height of 102m. [2] 
In the vicinity of the turbine in a distance of 290m, 
which is equal to 2.5D, measurements of the wind 
speed, wind direction, air temperature and air 
pressure are executed by means of standard 
measurement devices installed on a met mast at 
hub height. 
2.2 Wind field analysis 
The LIDAR scanning device allows the application 
of complex trajectories [3] and a following data 
processing of the wind field’s grid points so that one 
gains an enhanced estimation of the incoming wind 
field. The measured data gives information about 
wind speed, wind shear, wind direction and 
turbulence intensity over the complete rotor area.  
This paper is based on data collected with a so-
called 7x7 grid which comprises a 0,75D x 0,75D 
square swept by the scanner beam in 1D distance 
to the wind energy turbine. A sketch of this 
trajectory can be seen in Figure 2. This distance has 
Category: LIDAR 
been chosen due to the fact that the deployed 
LIDAR system has some limitations of its carrier to 
noise ratio (CNR) for larger scanning distances. In 
addition the blocking effects of the wind turbine are 
affecting the inflow wind field only to a small degree 
in a distance of 1D. Additional information about 
other possible trajectories is given in [4]. 
 
Figure 2: Illustration of the 7x7 grid trajectory 
2.3 LIDAR data preprocessing 
Because of temporal and spatial differences of each 
scanned grid point the data collected with the 
LIDAR device has to be preprocessed for a 
statistical analysis that applies to the current 
standards. A full documentation of the data 
conversion can be found in [4]. For this research 
two different data preprocessing methods have 
been used. The first method is a moving average 
algorithm which has been developed in [5]. The 
second method is a temporal and spatial 
interpolation method which is discussed in [3]. 
2.4 Data basis 
The following analysis is based on four different 
wind speed measurements and computations. The 
wind speed vmetmast applies to the data measured 
with the hub height anemometer, vaverage applies to 
LIDAR data generated with the moving average 
algorithm mentioned before, vinterpolated defines data 
generated with the interpolation algorithm and 
vinterpolated_50 applies to LIDAR data generated with 
the interpolation algorithm. In this case only the 
inner points (see Figure 2) are taken into account 
which could be considered as a reduction to a 5x5 
grid. This was done due to the fact that the 
measurement of the outer grid points with the 
LIDAR beam implies a higher angle of attack 
between the beam and the moving air particles. This 
increases the incertitude of the measurement 
especially because those grid points comprise, due 
to the large differences of the wind field on the outer 
edges of the measured wind profile, the biggest 
deviation to the measurements on hub height. Other 
data preprocessing tools developed for the LIDAR 
data have also been applied to exclude false data 
sets [4].  
A good impression of this wind profile deviation and 
of the benefit which can be gained by a scan of the 
complete wind field is shown in Figure 3, where a 
coloured plot displays the interpolated mean wind 
speed between the 7x7 grid points of the wind field 
for a 10-min time series. In this plot we can e.g. 
diagnose a deviation of ~4௠௦  between wind speed 
velocities on the outer edges of the wind field. 
 
Figure 3: Illustration of the wind speed distribution of an 
interpolated wind field for a 10-min time series  
To get a visual impact of the averaging methods 
which have been applied to the data, Figure 4 
illustrates exemplarily a 10-min time series with all 
mentioned wind speeds displayed. It is obvious that 
large fluctuations of the wind field are already 
averaged to a certain degree by the probe volume 
and the preprocessing of the LIDAR data. Of course 
it has to be added that the LIDAR scans in this 
special case a wind field which has passed the met 
mast ~20s ago. A time shift has been neglected 
because only the 10min mean values are of 
relevance for the data evaluation and it will be 
shown that it is not important for such a statistical 
analysis. 
 
Figure 4: 10-min time series 
For this study only data sets which apply to the 
definitions of Normal Operation Cases (NOP) 
defined in the IEC standards are taken into account. 
Other data sets have been filtered out. Due to the 
fact that other wind energy turbines and several tall 
structures in the vicinity of the turbine are limiting 
the valid measurement range only data sets from a 
small sector, where the wind direction is deviating at 
most 20° to the left and to the right of the met mast, 
have been considered for the research. Due to the 
small sector and after the filtering for valid time 
series only a small number of time series could be 
taken into account, i.e. the following analysis is 
based on 269 time series of 10 minutes which 
comprise approximately 10% of the whole data set. 
Category: LIDAR 
3 Capture Matrices 
The preprocessed LIDAR data and the measured 
data of a standard anemometer are classified 
according to their mean wind speed and turbulence 
intensity values for each 10-min time series into a 
capture matrix for normal power production.  
Figure 5 visualizes the mentioned matrices for the 
different wind speeds. 
 
Figure 5: Display of the capture matrices 
It is clearly visible that the matrices 2-4 that are 
based on LIDAR data differ from matrix 1 which is 
based on the anemometer data whereas there are 
only small differences noticeable between the 
LIDAR based matrices. If we sum the turbulence 
intensity and wind speed bins, it becomes obvious 
that the difference between the matrices results 
mainly from the turbulence intensity and not from 
the mean wind speed. 
 
Figure 6: Comparison of wind speed, linear regression and 
correlation factors 
If we do now plot the turbulence intensity and the 
mean wind speed as correlation diagrams, with a 
scatterplot in the lower corner and the appropriate 
coefficient of determination R² in the upper corner, 
for all possible comparisons (see Figure 6-7) we can 
state that the mean values of the wind speed differ 
only to a slight degree between the LIDAR and the 
anemometer measurements. But it can also be 
noticed that there are distinctive differences in 
turbulence intensity between both measurement 
methods. 
The reason for this difference is on the one hand 
due to the spatial averaging effects of a pulsed 
LIDAR system which is discussed in [6]. On the 
other hand it is clear that an averaging and an 
interpolation of a whole wind field will result as a 
different standard deviation i.e. a lower turbulence 
level than for a single anemometer. This “averaging” 
also results in a lower temporal resolution of the 
data which reduces the standard deviation of the 
wind speed and thereby the resulting turbulence 
intensity. 
 
Figure 7: Comparison of turbulence intensity, linear 
regression and correlation factors 
4 Fatigue analysis with LIDAR 
Based on the above-specified capture matrices a 
fatigue evaluation of the wind energy turbine’s 
components has been carried out for each wind 
speed. This paper focuses on the resulting fatigue 
of the edgewise and the flapwise root bending 
moment of a blade of the wind energy turbine. 
4.1 Loads estimation 
As a first step a rainflow counting of the loads for 
each time series has been carried out according to 
[1]. In a second step these rainflow counts have 
been weighted with the wind field data of the four 
presented wind speeds and the Weibull distribution 
of the test site, with Weibull factors ܣ ൌ 8.8 and 
݇ ൌ 2.41. The results are so-called Markov-Matrices 
for each time series that can be accumulated. The 
load spectra have been calculated based on these 
summation matrices. 
Category: LIDAR 
If the different load spectra are plotted based on the 
four different wind speeds it can be noticed that all 
four load spectra (see Figure 8) for both bending 
moments coincide very well. This was expectable 
due to the fact that a weighting of the data based on 
a Weibull distribution as it is designated in the IEC 
standard only weights the data according to the 
mean wind speeds. The turbulence intensity is not 
taken into account. 
Only for large oscillations small deviations are 
distinguishable in the load spectra display. This is 
because of the fact that these fractions of the 
spectra depend a lot on the wind speed and the 
resulting loads of the turbine close to rated speed 
which differ a bit between the two measuring 
methods. Low wind speeds and the differences 
between met mast anemometer and LIDAR in 
measuring them, do not affect the load spectra to a 
large degree. 
 
 
Figure 8: Comparison of load spectra (a) edgewise blade 
bending moment (b) flapwise blade bending moment 
Another impression of the good agreement of the 
loads based on anemometer and LIDAR 
measurements can be gained if we regard the 
damage equivalent loads (DELs) which are 
presented in Table 1 for a Wöhler value of ݉ ൌ 12 
and a reference number of cycles of ܰ ൌ 10଺.  
 v_metmast v_average v_interp. V_interp.50 
Edgewise 
bending 
moment 
1.000 1.0004 0.9987 0.9987 
Flapwise 
bending 
moment 
1.000 1.0059 1.0001 1.0001 
 
Table 1: Comparison of DELs 
We can state that the moving average algorithm 
estimates the wind speed and the resulting DELs 
better for the edgewise bending moment, whereas 
the interpolation algorithm gives better results for 
the flapwise bending moment. However both 
algorithms show a very good performance because 
of the maximum deviation of the damage equivalent 
loads of ~0.6%. 
5 Conclusion and Outlook 
It could be shown that a calculation of load spectra 
and damage equivalent loads based on 
measurements of the wind field by means of a 
nacelle-based LIDAR system is possible if certain 
computation methods of the wind field statistics are 
applied to the data. It is obvious that a current 
LIDAR system collects information about the wind 
differently as do standard anemometers. For this 
reason the standard deviation and so the turbulence 
intensity differs quite a lot between the two 
methods. Therefore the question arises whether e.g. 
the weighting of the statistical data of a rainflow 
count should also include the turbulence intensity if 
LIDAR devices are applied for measurements. But 
in contrast to standard methods it is possible to gain 
knowledge about the whole incoming wind field. 
Further studies will investigate if other trajectories 
with less scanned points per trajectory are as well 
comparable to standard wind measuring procedures 
for a statistical analysis of mechanical loads. With 
respect to the different sectors of the wind field the 
load characteristics of the turbine resulting from 
these variable inflow conditions will be researched 
in upcoming studies. The influence of special inflow 
conditions such as variable wind profiles for different 
times of the day or season could be of high interest 
for the certification of design driving components of 
a wind energy turbine. 
6 Acknowledgements 
This work is part of the research project “Further 
development of LIDAR measurements for the 
offshore test field” which is funded by the German 
Environment Ministry under the code number 
0327642, within the RAVE project. 
References 
[1] International standard, IEC 61400-13 “Wind 
turbine generator systems – Part 13: 
Measurement of mechanical loads, First edition 
2001-06 
[2] Rettenmeier, A. et al., Wind field analyses 
using a nacelle based LIDAR system, EWEC, 
Warsaw, 2010 
[3] Schlipf, D. et al., Development of a Wind 
Turbine LIDAR Simulator, EWEC Marseille, 
2009 
[4] Schlipf, D. et al., Testing of Frozen Turbulence 
Hypothesis for Wind Turbine Applications with a 
Scanning LIDAR System, ISARS, 2010 
[5] Schlipf, D. et al., Load Analysis of Look-Ahead 
Collective Pitch Control Using LIDAR, DEWEK, 
Bremen, 2010 
[6] Mikkelsen, T., On mean wind and turbulence 
profile measurements from ground-based wind 
lidars: limitations in time and space resolution 
with continuous wave and pulsed lidar systems, 
EWEC, 2009 


Statistical load estimation using a nacelle-based lidar system

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Statistical load estimation using a nacelle-based lidar system

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