Optimization of a wind turbine vibration-based SHM system

Transcription

1 8th European Workshop On Structural Health Monitoring (EWSHM 16), 5-8 July 16, Spain, Bilbao Optimization of a wind turbine vibration-based SHM system More info about this article: Gustavo OLIVEIRA 1, Filipe MAGALHÃES 1, Álvaro CUNHA 1, Elsa CAETANO 1 1 CONSTRUCT/ViBest, Faculty of Engineering, University of Porto, Portugal gustavo.oliveira@fe.up.pt, filipema@fe.up.pt, acunha@fe.up.pt, ecaetano@fe.up.pt Keywords: Onshore Wind Turbine, Operational Modal Analysis, Damage Detection Abstract This paper describes the main results obtained with different strategies of instrumentation of a. MW wind turbine, within the scope of establishing an optimized dynamic monitoring system. The data processing strategy is based on the continuous tracking of the main vibration modes of the instrumented wind turbine using Operational Modal Analysis (OMA) techniques combined with algorithms that deal with the particularities of wind turbines and that permit an online automated identification. Aiming to reduce the investment in monitoring equipment and simultaneously keep and adequate accuracy in structural condition assessment, in this paper, a one year database of continuously collected acceleration time series is used to evaluate alternative layouts for the distribution of the accelerometers along the tower height, using a varying number of installed sensors. 1 INTRODUCTION The recent trend in the wind energy industry has been based on the increasing of the power-output of the generators, alongside with the increase of the rotor and support structure dimensions. These characteristics have resulted in increasingly flexible structures subjected to higher dynamic forces, which demand an accurate assessment of their structural integrity, in order to guarantee the proper operation of these extremely expensive assets. The present paper is focused on the analysis of the results provided by a vibration-based monitoring system, suited for both onshore and offshore wind turbines, with alternative numbers of sensors and consequently with varying costs. The implemented and applied data processing is based on Operational Modal Analysis (OMA) techniques [1] and aims the identification of structural changes (i.e. damage) at an early stage and the estimation of the fatigue lifetime (not discussed in the present paper) of the instrumented structure. Within this system, the modal properties of the structure (natural frequencies, modal damping ratios and mode shapes) are evaluated throughout the different operating conditions of the wind turbine. By tracking abnormal variations of these properties, the occurrence of structural changes can be flagged. In order to reduce the investment in monitoring equipment and simultaneously keep an adequate accuracy in both damage detection and fatigue evaluation, a database collected during one year of monitoring is used to evaluate alternative layouts for the distribution of a varying number of accelerometers along the tower height: 3 bi-axial accelerometers at three levels; bi-axial accelerometers at levels; one bi-axial accelerometer at the tower top or one biaxial accelerometer at an optimized position. In this paper, only the results obtained for damage detection are exposed.

2 WIND TURBINE The adopted case study in this work is a. MW onshore wind turbine, located at the north of Portugal (Figure 1). The wind turbine presents a 8 m rotor diameter with variable speed operation supported by a steel tubular tower. The hub is located at 8 m high. Figure shows the power curve of the generator, in which the different operating conditions of the turbine are discretized. These different conditions are important in the definition of the data processing strategy, since they may introduce important variations in the modal properties of the wind turbine Power [kw] Wind speed [m/s] Figure 1: Wind turbine Figure : Power curve 3 MONITORING SYSTEM The adopted database was collected by a dynamic monitoring system composed by 9 uniaxial accelerometers distributed along the tower height and foundation level according to Figure 3. It was configured to continuously record acceleration time series of 1 minutes with a sampling rate of 5 Hz (subsequently decimated to 5 Hz). This data is complemented with the information collected by the SCADA system regarding the operational and environmental conditions. The analysis of the data collected by the full monitoring system is detailed presented in []. This paper focus on the results that can be obtained using the four different layouts of acceleration sensors summarized in Table 1. is based on the use of a single pair of accelerometers at the tower top (sensors S8 and S9). This option represents the most interesting solution since nowadays wind turbines are equipped with a bi-axial accelerometer sensor at this position and, thus, no additional investment in equipment is required. represents an optimization of. Due to the difficulty in the identification of the nd tower bending mode with, this second option optimizes the selection of the single level of measurement. The sensors S5 and S6 were used to simulate this solution. represents a natural consequence of the two previous solutions, being a combination of the use of the two pairs of sensors at the top (sensors S8 and S9) and around /3 of the tower height (sensors S5 and S6). Assuming that the installed bi-axial sensor at the top can be used together with other sensors, the cost of this solution would be similar to the cost of.

3 Lastly, a fourth layout using three levels of measurement was used with the objective of assessing the degree of accuracy of the other three optimized layouts S8 S6 S5 S3 S1 S9 S7 S S S1 S3 S S S6 S7 S9 S5 S8 Layout Nº of bi-axial Measurement Sensors levels a) b) Figure 3: Position of the accelerometers at the different levels of the wind turbine: a) front view; b) top view of the instrumented sections Table 1: Tested sensors layouts METHODOLOGY FOR DATA PROCESSING The vibration-based monitoring system is based on a modal approach [3]. In that sense, the modal parameters of the wind turbine structure (natural frequencies, damping ratios and mode shapes) are continuously tracked over time. As damages are normally associated with stiffness reduction, and assuming a negligible variation of mass (which is perfectly admissible on wind turbines), abnormal reductions of the natural frequencies are assumed as being motivated by damage. The monitoring system uses 1 min. acceleration measurements and SCADA data as inputs of the data processing algorithms. The acquired data is processed according to the scheme presented in Figure, which also includes reference to the routines developed for fatigue assessment (not described in the present work). Having in mind the early detection of damage, the following steps are performed: 1. Pre-processing the acquired acceleration data is decimated to focus the analysis in the.5 Hz range. Then, a coordinate transformation is applied to this data to obtain signals that are always aligned to the fore-aft (FA) and side-side (SS) directions.;. Automated modal analysis the acceleration data is processed using two output-only OMA algorithms: Covariance driven Stochastic Subspace Identification (SSI-COV) [] and poly-least Squares Complex Frequency Domain (p-lscf) [5], [6]. The results obtained with these methods are assessed, in an automated way, through a cluster analysis. Lastly, a modal tracking procedure is used, in which the properties of the considered clusters are compared against reference properties of the vibration modes, in order to identify the clusters referred to these modes; 3. Minimization of the operational and environmental effects on the natural frequencies of 3

4 the identified vibration modes. A detailed description of this step can be found in [];. Lastly, the detection of damage is performed using multivariate control charts. With this statistical technique, abnormal variations of the natural frequency values are detected. For each new data set, the control chart is updated with the inclusion of the result from the processing. Acceleration Data SCADA Data Signal Decimation Coordinate Transformation Automated Modal Analysis Modal Identication Cluster Analysis Fatigue Assessment Database Reference Modal Properties Modal Tracking Multivariate Regression Model Removal Operational/ Environmental Conditions Damage Detection Accumulated Damage/ Lifetime Prediction Figure : Main steps of the vibration-based monitoring system (related to damage detection) 5 RESULTS FROM ONE YEAR OF MONITORING In this section, special focus is given to the results obtained with the optimized instrumentation layouts (layouts 1 to 3). The results achieved using layout are used mainly to assess the quality of the optimized solutions. The main results obtained within the monitoring program using the full instrumentation layout can be accessed in [7]. 5.1 Modal Identification The analysis of the results obtained with the full instrumentation layout allowed the identification of 9 vibration modes, among rotor blades and tower bending modes. Figure 5 illustrates the tower configuration of three different vibration modes: 1 FA and FA (first and second tower bending mode in the FA direction); and 1 SS* (rotor blades vibration mode with movements of the tower).

5 1 FA FA 1 SS* Figure 5: Wind turbine tower mode shape for 1 FA, FA (both bending tower modes) and 1 SS* (rotor blades mode which includes tower motion) The evolution of vibration modes throughout the different operating regimes is usually presented in Campbell diagram, where this modal property is plotted against the rotor speed. Figure 6 a) presents the Campbell diagram obtained using the solution. It is visible that the 9 tracked vibration modes are well-identified with the exception of the second pair of tower bending modes ( FA and SS modes). This misidentification is due to the very low modal amplitude of these modes at the tower top (the position of the pair of sensors used with ). For that reason, these two modes were not considered in the condition assessment. Notwithstanding, it is interesting to note that the influence of the harmonics is eliminated with the adopted tracking procedure. On the other hand, Figure 6 b) introduces the Campbell diagram obtained with. From this figure, it is observed the good quality of the identification of the second pair of tower bending modes, confirming the suitability of the measurement location. On the other hand, it is visible that some clusters considered as from the 3 SS* mode are, in fact, corresponding to the 15Ω harmonic. However, this situation only occurred for a very small number of situations. As for the other vibration modes, no further erroneous modal identifications were detected. As expected, the results obtained with the third instrumentation layout showed the best accuracy among the optimized layouts. Campbel Diagram Campbel Diagram FA/SS 3 SS.5 FA SS* SS* Rotor Speed [RPM] SS FA.5 SS* SS* 1 SS.5 1 FA 3 FA/SS SS.5 FA 3 SS* Frequency [Hz] Frequency [Hz].5 FA 3 SS* 1 FA 18 a) Rotor Speed [RPM] 1 1 b) Figure 6: Campbell diagram with the tracked vibration modes with: a) and b) X FA fore-after modes; X SS side-side modes; X SS* rotor modes

6 The success rate achieved with the different layouts for the three tested modal identification algorithms is introduced in Figure 8. Due to the different level of accuracy obtained in the definition of the mode shapes with the different solutions, different modal tracking criteria were used for solutions with one bi-axial sensor ( and ) and with more than one bi-axial sensor ( and ). In the former case, a MAC criterion of.5 was required when comparing the similarity of the identified mode shapes with the reference properties of the modes; while a minimum MAC value of.8 was used for and. For that reason, separate analysis for each group must be conducted when analyzing the success rate obtained with the different solutions. Furthermore, higher success rates do not mean more accurate modal estimates. Comparing the results obtained for and, it can be concluded that the modes whose modal amplitude at the tower top is clearly superior to the other sections (as is the case of the 1 SS, 1 FA and 1 SS* modes) were better identified with. On the other hand, as expected, has shown to be more appropriate to identify vibration modes with large modal amplitude around /3 of the tower height ( FA, SS, 3FA/3 SS, 3 SS* and FA modes). represents a combination of the best results from the other two layout solution, being able to detect all the important wind turbine vibration modes with adequate accuracy. It is also noted that, in some cases, the success rate obtained with this layout is higher than for the layout. However, the results obtained with the three levels of measurement showed a smaller variability, which may indicate that these results are more accurate. 1 1 SS 1 1 FA 1 1 SS* SS* 1 FA 1 SS Figure 7: Success rate [%] obtained with the different identification algorithms according to the different layouts ( and, MAC limit of.5; and, MAC limit of.8). 5. Damage Detection In order to attest the suitability of the different layout solutions to detect the existence of damage in the wind turbine structure at an early stage, three different damage scenarios were 6

7 tested []. Damage scenario D1 assesses the ability of the system to detect scour problems at the foundation of offshore monopile wind turbines. The results presented in [8], which analyze the evolution of the first natural frequency of the support structure with the scour depth, were used to simulate the problem. A small scour depth of around.75 times the base diameter of the monopile is considered. This value is considered smaller than the recommended design value of 1.3 times the monopile diameter recommended in [9] and corresponds to a reduced scour length of around. m for typical monopile solutions. According to the referred study, this damage corresponds to a decrease of the first tower bending mode of around. %. Since nothing is said about the evolution of the remaining modes, especially the second support structure bending mode, results of the studies introduced in [1] and [11] were considered. These two studies show a ratio between the decrease of the second support structure bending mode relative to the first tower bending mode of.1 times to.6 times, respectively, for monopile foundations. In that sense, a conservative value of. was used to simulate the influence of this damage scenario in the dynamic properties of the wind turbine. Since it is not expected that rotor blades modes are influenced by this damage, their frequency values is not changed. The damage scenario D is related to an onshore foundation problem, related to the connection between the steel tower and the concrete foundation. It reproduces the frequency variation detected in [1]. This study describes the dynamic tests performed in three wind turbines, from which two of them presented anomalous vibration levels, together with several cracks at the foundation. The test indicated that the frequency values of the identified vibration modes from these two turbines were consistently lower than the ones from the third healthy turbine. Within the scope of damage scenario D, variations 5 times smaller than the variations reported in the study for the first and second tower bending modes were used. Again, the frequency value of the rotor blades modes was not changed for this scenario. The last damage scenario (D3) is related to blade damage. As referred in some studies [13] [1], wind turbine blades usually show a small sensitivity of the natural frequencies of the blade modes to small, common damages. Still, the monitored wind turbine was modelled with the help of the HAWC code [15] aiming to assess the sensitivity of the 1 SS* and SS* modes to structural damage at the blades. Considering that the eigenvalue analysis performed by the HAWC code is related to the turbine with the blades according to an operating configuration but under parked conditions, the expected frequency value of these two modes was used to tune the stiffness of the blades. Due to the high uncertainties in modelling the structural elements located inside the nacelle, as well as the rotor blades, the estimation of the frequency value of the 3 SS* mode was not possible. The comparison between the frequency values obtained with the numerical model and with the monitoring system is introduced in Table. Natural frequency [Hz] Modes Monitoring HAWC 1 SS* SS* Table : Comparison between the expected frequency values of the 1 SS* and SS* modes using the monitoring data (for parked conditions) with the results obtained with the HAWC model The damage scenario D3 is referred to a similar deterioration of three blades, which may be caused by continuous wear of the blades due to operation. The stiffness of the three blades 7

8 was decreased 15 % over a length of m, corresponding to 5 % of the total length of the blade. The damage was located at around 33 % in chord length from the blade root, which is said to be the location more prone to damage [16]. The imposed variation of stiffness led to a decrease of -.65 % of the 1 SS* and SS* rotor vibration modes. It was also noticed that the imposed stiffness variation was not significantly reflected in the other modes. For that reason, only the rotor modes 1 SS* and SS* were used to detect this damage scenario. The variation of the natural frequency values of the vibration modes with the defined damage scenarios are summarized in Table 3. The monitoring database was split into two different periods, consisting each one of data from intercalary days. With this strategy, both periods contain results within a period of one year, but from different days. Period 1 was then used to define a suitable multivariate regression model to minimize the influence of the operational and environmental factors, while Period was used to assess the quality of the model and for testing the algorithm for damage detection []. It is considered that the damage associated to each scenario occurred at the middle of Period. The artificial damage is thus introduced in the identified frequencies from the second part of Period by reducing their values according to the values presented in Table 3. f [%] Modes D1 D D3 1 SS FA SS*...65 SS* FA SS -... Table 3: Variation of the natural frequencies associated with the damage scenarios The methodology implemented to detect the presence of damage is based on the construction of control charts. The residual error between the identified and forecasted value of natural frequencies of the tracked modes provided by the previously trained regression models was used as input of the control charts. The results obtained for an undamaged situation and the three damage scenarios are presented in Figure 8. In their construction, groups with 36 observations were considered (corresponding to a quarter of a day). An upper limit was defined by considering that 95 % of the values from the training period (Period 1) are considered within the safety region. It is seen that the 3 considered damages are clearly detected using layouts, 3 and. Due to the misidentification of the second pair of tower bending modes using, it was not possible to detect the damage scenario D1 with this sensor layout. Still, the damage D was identified. The frequency variation of the first bending mode associated with this damage corresponds to a more severe scour damage, representing a depth of.16 times the base diameter of current monopiles, which still represents a very early state of damage, being considerably lower than the recommended design value of 1.3 [9]. 8

9 3 Undamaged scenario 3 Undamaged scenario 3 Undamaged scenario 3 Undamaged scenario D1 3 D1 3 D1 3 D D 3 D 5 D 3 D D3 5 5 D3 D3 3 D Figure 8: control charts associated with a scenario where no damage is present and with the three defined damage scenarios D1, D and D3 6 CONCLUSIONS The paper introduces the main results obtained with a vibration-based monitoring system aiming to detect small structural damages in wind turbines. Four different layouts of sensors were tested, with solutions considering one, two or three bi-directional sensors. Three different common damage scenarios were used to assess the ability of the system to detect these kinds of damages. It was concluded that the system is capable of detecting different types of damage at an early stage, even with a reduced number of installed sensors. However, when using the tower top as a single measurement level, a reduced level of precision was obtained in the detection of the simulated damage scenario of scour. Further research work is planned to quantify the time needed to detect each damage scenario with each layout and the influence of the sensors noise level in the results. REFERENCES [1] F. Magalhães, Á. Cunha, Explaining operational modal analysis with data from an arch bridge. Mechanical Systems and Signal Processing, 5, , 11. 9

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