operational effects on dynamic properties

Engineering Structures xxx (2015) xxx–xxx

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Vibration-based structural health monitoring of a wind turbine system Part II: Environmental/operational effects on dynamic properties Wei-Hua Hu ⇑, Sebastian Thöns, Rolf Günter Rohrmann, Samir Said, Werner Rücker ⇑ Federal Institute for Materials Research and Testing (BAM), Berlin, Germany1

a r t i c l e

i n f o

Article history: Available online xxxx Keywords: Vibration Structural health monitoring Wind turbine Environmental/operational effects Modal properties Principal Component Analysis Novelty analysis Damage detection

a b s t r a c t The second part of these companion papers mainly researches environmental/operational influences on structural dynamic properties under normal operational conditions during two years, in order to extract a statistical based damage-sensitive indicator for health monitoring of a wind turbine system. The correlation analyses between experimental identified frequencies, damping values as well as mode shapes and environmental/operational factors such as rotation speed of blades, wind speed, pitch angle, temperature and nacelle direction are presented. It is observed that the frequency estimates are influenced by the nacelle position, the activation of rotor, the rotation speed of blades and the wind speed as well as the temperature. Regarding to the damping estimates, they are mainly associated with variation of the aerodynamic damping due to the increasing wind speed. Besides, the resonance phenomenon is also observed in higher modes. The harmonic frequencies due to blades passing by tower are found and the corresponding damping value decreases. Moreover, the mode shapes in some modes are strongly affected by the position of the nacelle. Subsequently, two types of simulated damage including the reduction of stiffness in both the rotor blade and the tubular tower are successfully detected by applying the Principal Component Analysis (PCA) based methods to these temperature-sensitive frequency estimates. Comparison of change of the extracted health features indicates that they are more sensitive with the tower damage. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Structural health monitoring (SHM) is a discipline that aims to identify the health of a mechanical system through its lifecycle. The damage state of a system can be described as a five-step process to answer following questions [1]: (1) Existence, is there damage in the system? (2) Location, where is the damage in the system? (3) Type, what kind of damage is present? (4) Extent, how severe is the damage? (5) Prognosis, how much useful life remains? In order to answer the questions regarding the existence and extent of damage, an efficient method has been developed and applied in the field of civil infrastructures, by removing the adverse environmental/operational effects on the modal properties and building a reliable statistical health indicator that is only sensitive to early structural modification [2–7]. It generally consists of three aspects: (i) observation of the structural performance continuously ⇑ Corresponding authors. Tel.: +49 30 8104 3204; fax: +49 30 8104 1727. E-mail addresses: [email protected] (W.-H. Hu), [email protected] (W. Rücker). 1 URL: http: http://www.bam.de/en/kompetenzen/fachabteilungen/abteilung_7/ fg72/index.htm.

under normal environmental and operational conditions using highly diverse sensors and instrumentation devices, (ii) evaluation of structural behaviour and extraction of damage-sensitive features from measured dynamic characteristics, and (iii) statistical analysis of extracted features and issue an alarm when the designated performance criteria are exceeded [8]. Recently, wind turbine manufacturers, owners and operators have shown increasing interest in the SHM technology. Since the wind turbine systems are installed in off-shore wind parks or high elevation mountain regions with harsh environmental conditions, application of SHM technology will save maintenance and repair costs throughout its 10–30 year lifecycle. Moreover, implementation of an SHM system will assist in understanding behaviours of wind turbines under normal operational conditions in order to improve efficiency and lifetime at reduced material investment. A comprehensive review about the SHM technology of wind turbines and the relevant damage mechanisms are summarized by Ciang et al. [9]. Although the damage occurring to the tower is common [10], most research has mainly applied the SHM technology to the rotor blade [11,12] and research of health monitoring of the tubular tower is still rare. In [13–15], Thöns et al. develops a numerical model for evaluating fatigue damage, serviceability limit

http://dx.doi.org/10.1016/j.engstruct.2014.12.035 0141-0296/Ó 2014 Elsevier Ltd. All rights reserved.

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state and ultimate limit state of the support structure of wind energy converters. Rücker et al. observe that variation of soil stiffness and scour phenomenon will lead to modification of boundary conditions and further induce change of natural frequencies [16,17]. Benedetti implements a stain sensor-based remote health monitoring system of a wind tower for detecting cracks and estimating residual fatigue life [18,19]. Carne introduces the application of Operational Modal Analysis (OMA) to different types of wind turbines [20]. Fritzen and Kraemer apply Stochastic Subspace Fault Detection (SSFD) method and multivariate AR model-based algorithm to a laboratory structure for damage detection and location [21]. Staino and Basu study vibration in wind turbines considering variation of the blades’ rotational speed [22]. However, hardly any prior works focus on research into the dynamic behaviour of a wind turbine system under normal operational conditions with the purpose of implementing a damage detection strategy to detect the existence and extent of the structural damage. One difficulty is lack of the large amount of high quality data continuously collected from a wind turbine system under the operational conditions over several years. Another one is that the wind turbine system is under a complex operational condition with the variation of wind velocity, rotation speed, nacelle position and temperature, which leads to the difficulty of extraction of the damage-sensitive features from measured dynamic characteristics. In this context, this paper focuses on the responses of the dynamic properties of a wind turbine system to the complex environmental/operational factors, in order to explain the observed dynamic behaviours. Meanwhile, the current research also attempts to build a statistical health monitoring indicator and look into its sensitivity to different kinds of possible structural modifications. Firstly, the 5-megawatt wind turbine, continuous dynamic monitoring system and long term operational modal analysis results are generally introduced. Subsequently, correlation analyses are performed between the identified frequencies, the damping values as well as the mode shapes in different modes and the measured environmental/operational variables. Besides the resonance phenomenon, it is also observed that the frequencies in different modes are mainly influenced by the nacelle position, the action of rotor blades and the temperature. Experimental identified damping values increase slightly with rising wind speed but drop when resonance occurs. Such phenomenon may be explained by the variation of the aerodynamic damping. Mode shapes in some modes are affected by the nacelle direction. After thoroughly researching of the environmental/operational influences, the Principal Component Analysis (PCA) method is applied to the frequency estimates around 12.16 Hz and 21.81 Hz that are only sensitive with the temperature. Novelty analysis of the residual errors of PCA is addressed for structural monitoring. Two types of damage are simulated by reducing the stiffness in the blade and at the top of the tubular tower. It is noted that the PCA-based method can efficiently remove the temperature effects and detect the simulated damages. The proposed health features are more sensitive with the simulated damage on the top of the tower. A vibration-based monitoring system is constructed, with the basic premise capacity to report the damage of the tower under operational conditions.

2. The wind turbine and continuous dynamic monitoring system The Areva Multibrid M5000 is a prototype of a 5-megawatt wind turbine system, as shown in Fig. 1(a) and (b). It was built and tested from 2007 in the first German offshore wind energy test

field in the North Sea, preparing for the production of the commercial offshore wind power system. The dynamic responses of the tubular steel tower are recorded by 8 accelerometers mounted on its internal surface at four different levels (Fig. 1(b) and (c)). The accelerometers can be divided into two groups (Fig. 1(d)): One consists of y1, y3, y5 and y7 along the secondary wind direction (SWD) and another one is composed of y2, y4, y6 and y8 along the main wind direction (MWD). The structural responses under normal operational conditions were synchronously measured by signal acquisition equipment HBM MGCplus and were continuously recorded with a sample rate of 50 Hz from 1st November 2007 to 31st October 2009. Only the first 8192 sampling points acquired by each accelerometer at the beginning of each hour are recorded. Two groups of acceleration responses are automatically processed by the poly-reference Least Square Complex Frequency domain (p-LSCF) method and the identified dynamic properties and numerical analysis results are listed in Tables 1 and 2. An environmental and operational measurement station was installed on the hub of the wind turbine. From 1st November 2007 to 30th October 2009, variables such as temperature, wind speed, blade rotation speed, pitch angle of blades and orientation of nacelle are also recorded simultaneously at the beginning of each hour for 8192 points with sampling frequency 1 Hz. The mean values of all 8192 samples of different environmental/operational factors are calculated. The variations of the environmental/operational factors and their relationships have been discussed in [23]. In the following parts, the influences of rotation speed, wind speed, pitch angle, ambient temperature and position of nacelle on estimated frequency, damping and mode shape in different modes are researched separately.

3. Environmental/operational effects on frequency Regarding the frequency and the damping, only the results estimated by accelerometers y1, y3, y5 and y7 along the SWD are introduced, since those extracted from sensors y2, y4, y6 and y8 along the MWD exhibit similar behaviours. The mode shape and the associated MAC value estimated by two groups of accelerometers along both the SWD and the MWD will be presented, because they are dependent on the relative position of the rotating nacelle. Moreover, in order to characterise the variation of the estimated modal properties at different operational conditions, the frequency estimates are artificially divided into three clusters representing low, medium and high rotation speeds with the range varying in (1) 0–1.0 rpm, (2) 1.0–14.0 rpm and (3) 14.0–14.9 rpm. These samples are also labelled as blue (low), medium (black) and high (red), respectively. Fig. 2(a) displays the identified frequencies around 3.26 Hz over a two-year span and no apparent long term fluctuation can be found. Fig. 2(b)–(g) prove that the frequencies are only sensitive with the nacelle position. The opposite tendency is noted in the correlation plots between the frequencies identified by two groups of accelerometers and the absolute angle of nacelle. When the nacelle is near 60° and 240° along the MWD, the frequencies estimated by accelerometers y1, y3, y5 and y7 are higher than those identified by accelerometers y2, y4, y6 and y8. By contrast, the inverse tendency is observed when the nacelle is close to 150° and 330°. Correspondingly, the identified damping values are also influenced by the nacelle position (Fig. 15 in Section 4), whereas the mode shapes are not subjected to its effect (Fig. 17 in Section 5). Such observation may be caused by the two closely-spaced fore-aft (FA) and side-side (SS) modes. Regarding the dynamic analysis of the wind tower, the FA mode refers to the vibration

Please cite this article in press as: Hu W-H et al. Vibration-based structural health monitoring of a wind turbine system Part II: Environmental/operational effects on dynamic properties. Eng Struct (2015), http://dx.doi.org/10.1016/j.engstruct.2014.12.035

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y1

y3

y6

y5

97m

y4

67m

y2

y7

30m

y8

(a) General overview

(b) Scheme of wind turbine and positions of 8 accelerometers North 0⁰ 330⁰ 300⁰

Tripod

60⁰

West

East

Nacelle

240⁰

y1,3,5,7

MWD y2,4,6,8 South

(c) Accelerometers on the internal surface

SWD 150⁰

(d) Plane view of the wind turbine and wind direction

Fig. 1. The prototype of wind turbine M-5000, positions of 8 accelerometers on the tower and wind directions.

Table 1 Statistical analysis of the modal parameters estimated by accelerometers y1, y3, y5 and y7. fFE (Hz)

Eigen frequency Mean value (Hz)

0.41 0.42 3.31 3.55 4.09 6.62 7.49 8.28 12.72 20.75

Damping ratio

MAC value

Std

Mean value (%)

Std

Mean value (%)

Std

0.41

0.007

0.96

0.925

99.91

0.021

3.26

0.056

1.73

0.719

99.21

0.061

4.02 6.47 7.50 8.15 12.16 21.81

0.078 0.086 0.043 0.051 0.042 0.032

1.51 1.12 0.36 0.39 0.35 0.11

0.604 0.516 0.218 0.319 0.125 0.052

96.29 91.77 78.59 85.77 92.75 52.54

0.108 0.195 0.332 0.199 0.155 0.390

Table 2 Statistical analysis of the modal parameters estimated by accelerometers y2, y4, y6 and y8. fFE (Hz)

0.41 0.42 3.31 3.55 4.09 6.62

Eigen frequency

Damping ratio

MAC value

Mean value (Hz)

Std

Mean value (%)

Std

Mean value (%)

Std

0.41

0.008

1.19

1.021

99.98

0.008

3.27

0.057

1.91

0.759

98.91

0.066

4.02 6.48

0.076 0.082

1.50 1.12

0.621 0.431

51.58 57.26

0.424 0.414

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(a) Variations of frequency estimates around 3.26 Hz

(b) Frequency estimates vs rotation speed

(d) Frequency estimates vs pitch angle

(f) Frequencies estimated by y1, y3, y5 and y7 vs absolute angle

(c) Frequency estimates vs wind velocity

(e) Frequency estimates vs temperature

(g) Frequencies identified by y2, y4, y6 and y8 vs absolute angle

Fig. 2. Environmental/operational influences on the variation of the frequency estimates around 3.26 Hz.

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along the nacelle direction while the SS mode is perpendicular to the nacelle direction. Fig. 3 shows two closely-spaced modes corresponding to 3.31 Hz and 3.55 Hz predicted by the finite element model listed in both Tables 1 and 2. Both of them reflect the bending mode of the tower but in two perpendicular directions. Fig. 3(a) shows the FA bending mode associated with lower frequency. The main vibration deformation is along the nacelle while the vibration component perpendicular to the nacelle is quite small. Conversely, in the SS bending mode related to higher frequency, it is observed that the main vibration is perpendicular to the nacelle as plotted in Fig. 3(b). Therefore, as the nacelle changes position along the MWD (60° or 240°), the SS mode identified by accelerometers y1, y3, y5 and y7 is associated with the higher frequency, while accelerometers y2, y4, y6 and y8 only detect the vibration mainly contributed by the FA mode associated with the lower frequency. By contrast, when the nacelle moves to the position along SWD (150° or 330°), the frequencies identified by accelerometers y2, y4, y6 and y8 are higher than those estimated by y1, y3, y5 and y7 because the former represents the SS vibration mode with higher values in these two closely-spaced modes. Moreover, the dependence of the damping values on the absolute angle described in Fig. 15 in Section 4 also suggests the existence of two closely-spaced modes. In addition, the mode shapes of these two closely-spaced modes are similar, though distributed in the perpendicular directions. Thus, the mode shape characterised by Modal Assurance Criterion (MAC) in Fig. 17 in Section 5 is not influenced by the absolute angel of nacelle. The variation of the frequency estimates around 4.02 Hz is illustrated by Fig. 4(a). It is observed that the identified frequencies drop frequently over a two-year span. Such a phenomenon can be explained by correlation analysis between the different environmental/operational factors and the frequency estimates. Fig. 4(b) indicates that the low blade rotation speed accounts for the frequency estimates with lower values. In Fig. 4(b), the activation of rotor blades at a speed of around 5 rpm results in a ‘jump’ of the frequency estimates from about 3.85 Hz to around 4.05 Hz. Afterwards, with the rotation speeds rising from 5 rpm to 14.9 rpm, the frequency estimates are observed to increase gradually. In Fig. 4(c), the frequency estimates leap from around 3.85 Hz to about 4.05 Hz with increasing wind speed. Correspondingly, the frequency estimates with lower values are observed with higher pitch angle as shown in Fig. 4(d). It may be inferred from Fig. 4(b)–(d) that the activation of rotor blades induces the sudden increase of the frequency estimates of the wind turbine system. Finally, no significant effects of temperature on the variation of frequency estimates are observed as plotted in Fig. 4(e).

5

By contrast, the frequency estimates around 6.47 Hz often rise over a two-year span as shown in Fig. 5(a). Inspection of Fig. 5(b) demonstrates that the frequency samples with higher values are associated with lower blade rotation speed. When the blades begin to operate at around 5 rpm, the structural frequencies drop from around 6.65 Hz to about 6.4 Hz. Subsequently, the frequencies increase slightly with the rotation speed rising from 5 rpm to 14.9 rpm. Meanwhile, Fig. 5(c) shows that the frequency estimates are mainly scattered into two clusters with increasing wind speed. The upper part with higher values represents the blades operating at lower speed. When the blades begin to rotate, the frequency estimates suddenly decrease and then gradually increase with rising wind speed. In Fig. 5(d), it is observed that the frequency samples with higher pitch angle are associated with lower rotation speed. Moreover, a slight influence of temperature on the frequency estimates is shown in Fig. 5(d). The frequency values gradually decrease with increasing temperature. Fig. 6 describes the long-term trend of the frequency estimates around 7.50 Hz over a two-year span. In Fig. 6(a), the identified frequencies are mainly scattered into two parts. One of them reflects the annual fluctuation under low and medium rotation speed (in blue and black), and another one focuses on the range from around 7.45 Hz to 7.50 Hz as the wind blade spins at higher speed (in red). Such variation may be explained by Fig. 6(b)–(e). On one hand, as the measured wind speed changes from 0 m/s to about 10 m/s, the blade rotation speed varies from 0 rpm to about 14 rpm and the pitch angle decreases from around 100° to near 0°. Under these operational conditions, the frequency estimates are only subjected to the temperature influence as illustrated in Fig. 6(e). On the other hand, with the wind speed rising above about 10 m/s, the blade rotation speed gradually increases from 14 rpm to the maximum rotation speed 14.9 rpm and the pitch angle changes from near 0° to about 20° in order to generate stable electrical power. Under such conditions, the identified frequencies drop dramatically due to the resonance frequency 30f (30 ⁄ 14.9 rpm/ 60 = 7.45 Hz) induced by the blades passing by the tower [17]. From Fig. 6(e), it is observed that the resonance frequencies are not subjected to the influences of temperature. Similar resonance impacts are also observed for the identified frequencies around 8.15 Hz plotted in Fig. 7. When the rotation speeds are higher than 14 rpm, the frequency estimates are excited by the blades passage frequency 33f (33 ⁄ 14.9 rpm/60 = 8.20 Hz). When the rotation speed is lower than 14 rpm, the indentified frequencies are mainly affected by the temperature variations as illustrated in Fig. 7(e).

Nacelle Nacelle

(a) 3.31Hz

(b) 3.55Hz

Fig. 3. Calculated mode shapes corresponding to 3.31 Hz and 3.55 Hz.

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Nov/2007

Feb/08 May/08

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Feb/09

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Time

(a) Variations of frequency estimates around 4.02 Hz

(b) Frequency estimates vs rotation speed

(d) Frequency estimates vs pitch angle

(c) Frequency estimates vs wind velocity

(e) Frequency estimates vs temperature

Fig. 4. Environmental/operational influences on the variation of frequency estimates around 4.02 Hz.

Fig. 8(a) and (a) describes the long term variations of identified frequencies around 12.16 Hz and 21.81 Hz. Inspection of the correlation analysis performed between them and the different environmental/operational factors demonstrates that they are only subjected to the temperature effects. Both of them decrease with increasing temperature over a two-year span, as shown in Figs. 8(b) and 9(b). 4. Environmental/operational effects on damping The experimental identified damping values in different modes over a two-year span are mainly influenced by the blade

rotation speed and the wind speed. Figs. 10–14 show the relations between damping estimates and rotation speed as well as wind speed for different modes. Tables 3 and 4 list the averaged damping values estimated under low (0–1.0 rpm) and high (14.0–14.9 rpm) rotation speed on the basis of accelerometers along both the MWD and the SWD. For the vibration modes associated with 3.26 Hz, 4.02 Hz and 6.47 Hz, it is observed from Figs. 10–12 that the identified damping values gradually increase with rising wind speed and rotation speed. According to results listed in Tables 3 and 4, the averaged damping values identified under high rotation speed (14.0–14.9 rpm) are all higher than those estimated under low rotation speed (0–1.0 rpm). However,

Please cite this article in press as: Hu W-H et al. Vibration-based structural health monitoring of a wind turbine system Part II: Environmental/operational effects on dynamic properties. Eng Struct (2015), http://dx.doi.org/10.1016/j.engstruct.2014.12.035

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Nov/2007 Feb/08 May/08

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Feb/09

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Nov/2009

Time

(a) Variations of frequency estimates around 6.47 Hz

(b) Frequency estimates vs rotation speed

(c) Frequency estimates vs wind velocity

(d) Frequency estimates vs pitch angle

(e) Frequency estimates vs temperature

Fig. 5. Environmental/operational influences on the variation of frequency estimates around 6.47 Hz.

inspection of Figs. 13 and 14 shows that with increasing rotation speed and wind speed, the damping values decrease, especially in the high rotation speed ranging from 14.0 rpm to 14.9 rpm. Table 3 shows that the reducing averaged damping values are 0.37% and 0.2% for both modes 7.50 Hz and 8.15 Hz. Such phenomena can also be explained by the damping mechanism considering the resonance effects. As presented in [23–25], the total damping ntotal estimated by OMA consists of the structural damping nstruct and the aerodynamic damping naero . The structural damping nstruct is a measurement of energy dissipation in the wind turbine system, and it is assumed to be constant with rising rotation speed and wind speed. The aerodynamic damping naero develops from the interaction between the wind and oscillating rotor. It depends on wind speed and the rotation speed.

Figs. 10–14 suggest that, with regard to these modes without the resonance effects, the aerodynamic damping is always positive and thus the total damping estimated by OMA increases with rising wind speed and rotation speed. However, for the modes subjected to the resonance effects due to the blade passing frequencies 30f and 33f (Figs. 6 and 7), the decreasing damping values with rotation speed ranging from 14 rpm to 14.9 rpm indicate negative aerodynamic damping caused by the resonance. Except for the effects from wind speed and rotation speed, the estimated damping values for the second bending mode around 3.26 Hz are also subjected to the influences of nacelle position as illustrated in Fig. 15. Again, it indicates the existence of two closely-space modes as discussed in Section 3.

Please cite this article in press as: Hu W-H et al. Vibration-based structural health monitoring of a wind turbine system Part II: Environmental/operational effects on dynamic properties. Eng Struct (2015), http://dx.doi.org/10.1016/j.engstruct.2014.12.035

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Nov/2007

Feb/08

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Time

(a) Variations of frequency estimates around 7.50 Hz

(b) Frequency estimates vs rotation speed

(c) Frequency estimates vs wind velocity

(d) Frequency estimates vs pitch angle

(e) Frequency estimates vs temperature

Fig. 6. Environmental/operational influences on the variation of frequency estimates around 7.50 Hz.

5. Environmental/operational effects on mode shape The mode shape of the tubular tower has a directional characteristic because the distribution of mass/mass moment of inertia changes due to the orientation of the nacelle. In order to evaluate the environmental/operational effects on the estimated mode shape, the following procedure is used for each individual mode, and its application to fundamental mode shown in Fig. 16 is taken as an illustrative example: (i) all identified mode shapes are normalized by scaling the maximum value to unit and the corresponding channel is defined as reference (Fig. 16(a) and (b)); (ii) the occurrence percentages of mode shapes normalized to different reference channels are listed in Tables 5 and 6; (iii) the dominant

mode shape is calculated by averaging all mode shapes with the highest occurrence percentage (Fig. 16(c)–(e)); (iv) the Modal Assurance Criterion (MAC) values between the dominant mode shape and every mode shape estimated by 8192 sampling points over a two-year span are computed; (v) finally, a correlation analysis is performed between the MAC value and the absolute rotation angle (Fig. 16(g) and (h)). It is shown from Fig. 16(a), (b) and Table 5 that, along both the SWD and the MWD, the cases are over 99.8% in which the maximum value of the identified mode shapes is from the top of the tower. By averaging the most frequently excited mode shapes shown in Fig. 16(c) and (d), nearly the same dominant mode shapes along both the SWD and the MWD are computed, as shown

Please cite this article in press as: Hu W-H et al. Vibration-based structural health monitoring of a wind turbine system Part II: Environmental/operational effects on dynamic properties. Eng Struct (2015), http://dx.doi.org/10.1016/j.engstruct.2014.12.035

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Nov/2007 Feb/08

May/08

Aug/08

Nov/08

Feb/09

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9

Aug/09 Nov/2009

Time

(a) Variations of frequency estimates around 8.15 Hz

(b) Frequency estimates vs rotation speed

(c) Frequency estimates vs wind velocity

(d) Frequency estimates vs pitch angle

(e) frequency estimates vs temperature

Fig. 7. Environmental/operational influences on the variation of frequency estimates around 8.15 Hz.

in Fig. 16(e), thereby agreeing with the calculated fundamental mode shape in Fig. 16(f). Subsequently, correlation analysis shown in Fig. 16(g) and (h) demonstrates that the rotation of nacelle has no clear influences on the estimated mode shapes within a twoyear span. Fig. 17 shows the influence of the nacelle rotation on the tower mode shape around 3.26 Hz. As shown in Tables 5 and 6, the occurrence percentages are over 99% when the maximum values of the mode shapes are from both y3 and y4. Fig. 17(a) and (b) plot the corresponding most excited mode shapes. Based on these, similar dominant mode shapes along SWD and MWD are computed and plotted in Fig. 17(c), thus coherent with the bending mode shape of the tower calculated by numerical analysis shown

in Fig. 17(d). Inspection of Fig. 17(e) and (f) indicates that the long-term variation of mode shape is not dramatically subjected to the influence of the changing position of nacelle during operational conditions. Figs. 18(a)–(c) describe the experimental identified mode shapes around 4.02 Hz over a two-year span. Along the SWD, only one type of mode shapes dominates and its occurrence percentage is 96.57% as listed in Table 5. By contrast, two types of main mode shapes are observed along the MWD and their occurrence percentages are 59.11% and 40.73%, respectively. The corresponding dominant mode shapes along both the SWD and the MWD are shown in Fig. 18(d). According to correlation analysis between the MAC value and the angle of the nacelle shown in Fig. 18(e) and (f), the mode

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Nov/2007Feb/08 May/0

Aug/0

Nov/08 Feb/09

May/0 Aug/0 Nov/2009

(a) Variations of frequency estimates around 12.16Hz

(b) Frequency estimates vs temperature

Fig. 8. Environmental/operational influences on the variation of frequency estimates around 12.16 Hz.

Nov/07

Feb/08 May/08

Aug/08 Nov/08

Feb/09

May/09 Aug/09 Nov/09

Time

(a) Variations of frequency estimates around 21.81 Hz

(b) Frequency estimates vs temperature

Fig. 9. Environmental/operational influences on the variation of frequency estimates around 21.81 Hz.

(a) Damping estimates versus rotation speed

(b) Damping estimates versus wind velocity

Fig. 10. Effects of rotation speed and wind velocity on the variation of damping estimates with frequency estimates around 3.26 Hz.

shape along the SWD is less influenced by the nacelle position while the mode shape along the MWD changes completely when the nacelle is at around 60° and 240°. The numerical analysis result is shown in Fig. 19. It indicates that such vibration mode combines bending and torsion behaviours of the tower. Similar phenomena are also observed in vibration mode around 6.47 Hz. It is also noted from Fig. 20 that the identified vibration modes along the MWD modify completely when the nacelle is at the position about 60° and 240°. The analogous vibration mode mixed with bending and torsion components is also illustrated in the numerical analysis results shown in Fig. 21.

In the higher modes, the corresponding vibration modes are not identified by the responses recorded by accelerometers y2, y4, y6 and y8. Meanwhile, the variations of the MAC values identified by the responses recorded by accelerometers y1, y3, y5 and y7 of these modes are not clearly dependent on the position of nacelle. 6. Feature extraction for health monitoring The primary object of vibration-based structural health monitoring techniques is to ascertain the appearance of early damage based on measured signals. However, the structures under

Please cite this article in press as: Hu W-H et al. Vibration-based structural health monitoring of a wind turbine system Part II: Environmental/operational effects on dynamic properties. Eng Struct (2015), http://dx.doi.org/10.1016/j.engstruct.2014.12.035

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(a) Damping estimates versus rotation speed

11

(b) Damping estimates versus wind velocity

Fig. 11. Effects of rotation speed and wind velocity on the variation of damping estimates with frequency estimates around 4.02 Hz.

(a) Damping estimates versus rotation speed

(b) Damping estimates versus wind velocity

Fig. 12. Effects of rotation speed and wind velocity on the variation of damping estimates with frequency estimates around 6.47 Hz.

(a) Damping estimates versus rotation speed

(b) Damping estimates versus wind velocity

Fig. 13. Effects of rotation speed and wind velocity on the variation of damping estimates with frequency estimates around 7.50 Hz.

operational conditions are inevitably subjected to changing environmental/operational conditions that influence the variability of modal properties, which may mask the subtle variations induced by structural damage. Thus, it is essential to remove the adverse environmental/operational effects and build a reliable health indicator that is only sensitive to early abnormal structural modification [2]. For this wind turbine system under complex environmental/operational conditions, the frequency estimates in different modes are influenced by resonance, nacelle position, activation of blade and temperature, which certainly cover the shifts of the frequency estimates induced by early damage. How-

ever, the frequency estimates around 12.16 Hz and 21.81 Hz are only influenced by the temperature. Meanwhile, correlation analysis between these frequency estimates of two modes (Fig. 22) suggests a relatively strong linear relation with a correlation coefficient of 0.78. Thus, Principal Component Analysis (PCA)-based method can be used to identify and remove the linear subspace due to the temperature effect. The current paper only presents a general model of PCA-based method for removing environmental/operational effects. The detailed description and application of PCA-based damage detection procedure can be consulted in [3–5].

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(a) Damping estimates versus rotation speed

(b) Damping estimates versus wind velocity

Fig. 14. Effects of rotation speed and wind velocity on the variation of damping estimates with frequency estimates around 8.15 Hz.

Table 3 Comparison of the averaged damping values, identified on the basis of the accelerometers y1, y3, y5 and y7, in different rotation speed ranges. Mean value of eigen frequencies (Hz)

3.26 4.02 6.47 7.50 8.15

Mean value of damping ratios (%)

Difference (%)

Low rotation speed (0–1.0 rpm)

High rotation speed (14.0–14.9 rpm)

1.51 0.79 1.01 0.43 0.39

2.19 2.13 1.59 0.18 0.19

+0.68 +1.34 +0.58 0.37 0.20

Table 4 Comparison of the averaged damping values, identified on the basis of the accelerometers y2, y4, y6 and y8, in different rotation speed ranges. Mean value of eigen frequencies (Hz)

3.27 4.02 6.48

Mean value of damping ratios (%)

Difference (%)

Low rotation speed (0–1.0 rpm)

High rotation speed (14.0–14.9 rpm)

1.46 0.71 0.93

2.41 2.16 1.43

(a) Damping values estimated by y1,y3, y5 and y7

+0.95 +1.45 +0.50

(b) Damping values identified by y2,y4, y6 and y8

Fig. 15. Damping values identified by two groups of accelerometers versus absolute angle of nacelle.

It is assumed that the fluctuation of frequency estimates Y mainly results from two components as:

perature and follow the similar trend. Thus, Eq. (1) can be transformed into:

Y ¼ f ðT; W . . .Þ þ gðgÞ

Y ¼ KT þ e

ð1Þ

where f ðT; W . . .Þ is a function of the environmental/operational variables (i.e., temperature T, Wind W,. . .), and g is a variable that is associated with structural damage and measurement noise. For the wind turbine system, the frequency estimates around 12.16 Hz and 21.81 Hz are only influenced by the variation of tem-

ð2Þ

where T is the measured temperature. K is a matrix reflecting the linear relation between the temperature and the frequency estimates. e is an independent residual representing damage and measurement noise. The term KT can be identified by applying the PCA method to the frequency estimates under operational conditions for

Please cite this article in press as: Hu W-H et al. Vibration-based structural health monitoring of a wind turbine system Part II: Environmental/operational effects on dynamic properties. Eng Struct (2015), http://dx.doi.org/10.1016/j.engstruct.2014.12.035

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(a) All normalized mode shapes along SWD

(c) The most frequently excited mode shapes along SWD

(b) All normalized mode shapes along MWD

(e) Dominant mode shapes along both SWD and MWD

(g) MAC values along SWD versus absolute angle of nacelle

(d) The most frequently excited mode shapes along MWD

(f) Calculated mode shape

(h) MAC values along MWD versus absolute angle of nacelle

Fig. 16. Effects of absolute angle of nacelle on fundamental mode shapes.

a full cycle when the structure is at a healthy stage. Thus, the environmental/operational effects can be efficiently eliminated and only the term e associated with structural damage and noise is kept. Eq. (2) can be expressed as

e ¼ Y  KT

ð3Þ

Certainly, the linear relation between measured temperature and frequency estimates can also be identified by a regression method. But the main difficulty is in determining the optimal location of thermal sensors because the temperature measurements from different positions have different influences for the frequency

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Table 5 Occurrence percentage of mode shapes along SWD normalized to different reference channels. Reference Percentage (%)

0.41 Hz 3.26 Hz 4.02 Hz 6.47 Hz 7.50 Hz 8.15 Hz 12.16 Hz 21.81 Hz

y1

y3

y5

y7

99.87 0.57 3.24 85.16 7.27 2.54 1.30 7.30

0.12 99.31 96.57 1.46 82.19 2.18 34.53 30.64

0.01 0.11 0.14 11.33 8.34 94.21 63.87 10.27

0.00 0.01 0.05 2.05 2.20 1.07 0.30 51.79

Table 6 Occurrence percentage of mode shapes along MWD normalized to different reference channels. Reference Percentage (%)

0.41 Hz 3.27 Hz 4.02 Hz 6.48 Hz

y2

y4

y6

y8

100 0.68 59.11 56.97

0.00 99.24 40.73 4.58

0.00 0.06 0.16 38.32

0.00 0.02 0.00 0.13

(a) The most frequently excited mode shapes along SWD

(b) The most frequently excited mode shapes along MWD

(e) MAC values along SWD versus absolute angle of nacelle

estimates. The PCA-based method can overcome such a drawback because the environmental effects are treated as embedded variables and can be identified directly from the frequency estimates without the temperature measurement [5,6]. Once the residual error e is computed, the Novelty Detection technique can be applied to detect early damage [7]. An internal representation Novelty Index (NI) is first built when the wind turbine system is under healthy conditions covering one full cycle of environmental/operational variations, and then data are subsequently examined to observe the possible occurrence of significant departure from the normal condition by using the outlier analysis. Two health features, a central line C L and an upper line UL, are extracted from the NI [26]:

C L ¼ NI

ð4Þ

UL ¼ NI þ ar

ð5Þ

where NI and r are the mean value and the standard deviation of the NI in the reference healthy state, respectively. a is taken as 3, which corresponds to 99.7% confidence. Two criteria are employed as damage warning: (1) the ratio of the mean values of NI between a healthy and a subsequent state; and (2) the percentage of NI lying outside the defined limit by outlier analysis. If the wind turbine is still healthy, the new frequency

(c) Dominant mode shapes along both SWD and MWD

(d) Calculated mode shapes

(f) MAC values along MWD

versus absolute angle of nacelle

Fig. 17. Effects of absolute angle of nacelle on mode shape with eigen frequencies around 3.26 Hz.

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W.-H. Hu et al. / Engineering Structures xxx (2015) xxx–xxx

(a) The most frequently excited mode shapes along SWD

(b) One type of mode shapes along MWD

(e) MAC values along SWD versus absoluteangle of nacelle

(c) Another type of mode shapes along MWD

15

(d) Dominant mode shapes along both SWD and MWD

(f) MAC values along MWD versus absolute angle of nacelle

Fig. 18. Effects of absolute angle of nacelle on mode shape with frequency estimates around 4.02 Hz.

(a) Calculated mode shape

(b) Top view of mode shape

Fig. 19. Numerical mode shape with frequency estimates around 4.02 Hz.

estimates of the subsequent state should stay in the hyper-plane spanned by the frequency estimates in the reference state, so the mean values of NI under the health/subsequent state should be approximately the same value, and the percentage of NI exceeding the defined upper limit should be small. Conversely, with the occurrence of damage, the new frequency estimates will depart from the hyper-plane in the reference state, which will result in

relatively large NI ratio under healthy/subsequent state and cause the percentage of NI outside the limit to increase significantly. As the wind turbine system just began to operate in 2007 and no obvious structural change is observed over the next two years. Meantime, it is no feasibility to introduce any artificial modifications to the wind turbine system under operational conditions. Thus, the possible damage is simulated based on reducing the stiffness of the hot spots in the finite element model. According to the fatigue damage analysis performed in [8], it is observed that the highest probability of fatigue failure is quantified at the top of the tubular tower, as shown in Fig. 23(a) and (c). Moreover, the resonance may be aggravated by the Sommerfeld phenomenon [23] and accelerate the fatigue failure. Thus, Damage II and Damage III are modelled by setting the stiffness of 26.6 and 53.3 degrees of the cross-sectional circumference as zero, which means a loss of 7.4% and 14.8% of the cross-section area. In addition, it is reported from both the simulation and the experimental methods that the locations at 30–35% along the length of the blade from the root section are more prone to damage [9]. While in [12], the damage to blade is simulated by decreasing the stiffness parameters with 10%, 25% and 50%. In current paper, 30% decrease of the blade stiffness near 30% length is used to simulate the possible damage on the blade. Thus, Damage I is simulated to reduce the 30% stiffness of the hot spot at the 30% length of the blade, as described in Fig. 23(a) and (b). The changes of the structural natural frequencies in percentage are listed in Table 7.

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(a) The most frequently excited mode shapes along SWD

(b) One type of mode shapes along MWD

(e) MAC values along SWD versus

absolute angle of nacelle

(c) Another type of mode shapes along MWD

(d) Dominant mode shapes along both SWD and MWD

(f) MAC values along MWD versus

absolute angle of nacelle

Fig. 20. Effects of absolute angle of nacelle on mode shape with frequency estimates around 6.47 Hz.

(a) Calculated mode shape

(b) Top view of mode shape

Fig. 21. Numerical mode shape with frequency estimates around 6.47 Hz.

Fig. 22. Linear relation between the frequency estimates around 12.16 Hz and 21.81 Hz.

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70% Location of the hot spots

30%

(a) Positions of the simulated damage in the wind turbine system Damage II 26.6º

Damage I

30%

70% Damage III 53.3º

(b) Damage I in the hot spot of the blade

(c) Damage II, III in the hot spot of the tower

Fig. 23. Different simulated damage scenarios.

The continuous monitoring results during the first year (from November 2007 to October 2008) are selected as reference, and then frequency estimates around 12.16 Hz and 21.81 Hz in the second year (from November 2008 to October 2009) are affected by loading the variations of these frequencies in order to simulate the change resulting from the considered damage scenarios. After removing the temperature effects, Novelty Index (grey points) and central lines C L (in different colors) in reference state and different damage scenarios are plotted in Fig. 24. The corresponding two damage criteria consisting of ratio of NI and outlier analysis are listed in Table 8. The upper limit (UL) is defined with CL, and r in the reference state is calculated on the basis of Eqs. (4) and (5). First of all, it is noted from Fig. 23 that no clear long term variations of Novelty Index are observed in the first two years and thus the adverse environmental effects are removed. Table 7 shows that the healthy feature CL extracted from November 2008 to October 2009 is only 1.02 and nearly at the same level as the one in the first year. Meanwhile, the outlier analysis over a two-year span also shows a similar trend. It may be inferred that, from November 2008 to October 2009, no obvious damage is observed and the wind turbine system continues in healthy condition. Secondly, it

is observed from Fig. 23 that the CL lines associated with three damage scenarios clearly deviate from the reference state. As listed in Table 8, the ratios of mean value of NI increase from 1.76, 1.92 to 2.51 and the outlier analysis also presents a similar trend. So, the simulated damage in different levels is efficiently detected. Finally, it is interesting to note from Table 8 that reduction of the 30% stiffness of the hot spot in the blade only resulted in the increase of the NI to 1.76 and 7% of the NI falling out of the UL. Both health

Table 7 Changes of frequencies caused by simulated damage (percentage of variation with regard to the numerical results).

Table 8 Outlier analysis and ratio of NI in different damage scenarios shown in Fig. 23.

Natural frequencies

fFE = 12.72 Hz

fFE = 20.75 Hz

Damage I Damage II Damage III

0.086% 0.102% 0.291%

0.414% 0.467% 0.805%

Nov07-Oct08 Nov08-Oct09

DamageI

DamageII

DamageIII

Fig. 24. Removal of temperature effect and detection of simulated damage.

Outlier analysis Ratio of NI

Nov/07Oct/08

Nov/08Oct/09

Damage I

Damage II

Damage III

1.4% 1

1.2% 1.02

7.0% 1.76

8.9% 1.92

19.6% 2.51

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features are smaller than those caused by the loss of 7.4% and 14.8% of the cross-section area of the tower section. It may be concluded that the proposed health features are more sensitive with the occurrence of the tower damage. 7. Conclusions With the purpose of building a structural health monitoring system for an in-service wind turbine, this paper thoroughly researches the dynamic behaviours of a wind turbine under operational conditions over a two-year span. Firstly, it is observed that the frequency estimates around 3.26 Hz vary with the nacelle position because of two closely-spaced modes whose mode shapes are similar but vibrate along perpendicular directions. The frequency estimates around 4.02 Hz and 6.47 Hz drop or increase frequently, which is mainly caused by the operation of rotor blades. Beyond the clear temperature effects, the resonance phenomenon is also noted in the frequency estimates around 7.50 Hz and 8.15 Hz due to the harmonic frequencies 30f and 33f as the blades’ rotation speed approaches 14.9 rpm. The frequency estimates around 12.16 Hz and 21.81 Hz are only influenced by the temperature. Regarding the damping estimates, they increase gradually with rising wind and rotation speed but reduce when the resonance occurs. Such a phenomenon may be explained by the variation of the aerodynamic damping with the changing blade rotation speed. Subsequently, the mode shapes corresponding to 4.02 Hz and 6.47 Hz are found to depend on the absolute angle of nacelle but the first two bending modes around 0.41 Hz and 3.26 Hz are free with such influence. Finally, health features are extracted by removing the temperature effects on the frequency estimates around 12.16 Hz and 21.81 Hz on the basis of the PCA-based method. Research into the extracted Novelty Index shows that the temperature effects are efficiently removed and no clear structural change is observed from November 2007 to October 2009. The damage in both the blade and tower is simulated based on a finite element model, and is further detected by the deviation of Novelty Index compared to the reference state. The wind turbine monitoring system developed in this paper not only assists in understanding the structural in-service dynamic behaviours but also can serve for long-term structural health monitoring with the premise capacity to detect the possible structural damage. In the future, further research will be developed in order to ascertain the minimum damage and estimate the corresponding residual life of the wind turbine system. In particular, the extreme load conditions, such as earthquake and wind gust, have to be considered. Acknowledgements The authors are grateful to the German Ministry of Economics and Technology for the financial support of the IMO-WIND project (Grant No. 161NO326), also to the Federal Institute for Materials Research and Testing (BAM VII-2) and to the company AREVAWIND for providing the measurement data. The first author acknowledges the Adolf Martens fellowship granted by the Federal Institute for Materials Research and Testing (BAM).

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