An Integrated SHM Approach for Offshore Wind Energy Plants

Claus-Peter Fritzen, Peter Kraemer and Maksim Klinkov University of Siegen, Department of Mechanical Engineering,

Paul-Bonatz-Str. 9-11, 57068 Siegen, Germany, fritzen@imr.mb.uni-siegen.de, kraemer@imr.mb.uni-siegen.de, klinkov@imr.mb.uni-siegen.de

ABSTRACT Global warming, the limitation of combustible resources and lack of public acceptance of nuclear power plants including the problem to find appropriate nuclear waste deposits, has pushed renewable sources of energy towards the top of the power generation agenda. This has helped to promote wind power as one of the most cost effective of the renewable technologies. Since the energy gain from off-shore power plants (OWEP) is higher as onshore, many offshore wind parks worldwide are projected. The other side of the medal is that the costs for inspections and maintenance offshore are much higher than for onshore plants. Especially under harsh condition on the sea, classical inspection methods are not practicable. For this reason it makes sense to develop monitoring systems in order to reduce the number of inspections, to identify damage in an early phase and to forecast the remaining life-time of OWEPs. The first part of the paper gives a short overview on the importance of SHM systems for OWEPs. In the second part some suitable methods for monitoring different parts of OWEPs are mentioned. Finally, methods for online force identification sensor fault identification and damage detection/localization accompanied on field tests of a 5 MW plant will be presented. INTRODUCTION: SHORT OVERVIEW OF MONITORING SYSTEMS FOR OWEP A large OWEP represents a big technical challenge with respect to design, operation and maintenance. It is a matter of fact that the downtime of the plant for extended repair and maintenance (harsh condition on the sea) resulting from undiscovered damage can lead to big economical losses. Costs models [1,2] for OWEPs show that operation and maintenance is a considerable factor in the total cost of a plant [3]. It has to be noted that, as wind farms are sited further offshore, vessel time will increase for each visit due to greater transport time and potentially higher risk of weather downtime. To reduce these costs, there is a need to develop a monitoring system which provides the operator necessary and reliable information about the plant’s state and allows the initiation of maintenance actions. Not only the blades and gearbox, but also the tower and foundation have to be monitored, because the knowledge about the structural loads resulting from wind and water waves and the resulting material stresses is not yet fully understood. For a better understanding of the monitoring of OWEPs the relevant parts are displayed in Figure 1. Here the foundation is based on a tripod construction, whereas this depends on the design philosophy of the producers. Other possible foundations are: monopile or jacket constructions. A very good overview on different constructions of WEP or OWEP and their dynamical behavior is given in [4]. SHM systems for different parts of OWEPs are in development since many years. The most monitored components today are the machine components and the blades. The gearbox, bearings, etc. are online controlled with methods derived from condition based monitoring (CBM). Since a lot more experience exist in CBM than in SHM, many companies and research institutes worldwide offer their services for monitoring machine parts, whereas this domain is dominated by the producers of the machine components. A big challenge remains for the monitoring of slowly rotating machine parts. Also the signal transmission between the sensors and the monitored parts is often long and the separation of the signals coming from many components simultaneously is therefore difficult. Monitoring of these parts is mostly done with accelerometers. There is a challenge to develop sensors for slow rotating machinery.

Proceedings of the IMAC-XXVIIIFebruary 1–4, 2010, Jacksonville, Florida USA

©2010 Society for Experimental Mechanics Inc.

Figure 1. Parts of OWEP. Example based on Multibrid M5000-2 offshore prototype

The most problematic part of the machine is the planetary gear with its complicated kinematics. A total damage of planetary gear follows by its exchange/repair on the sea is associated with immense costs. The monitoring of the rotor blades is still in development and only few companies offer online monitoring on the field. The applicability of a big palette of methods on rotor blades is tested today. E.g. Mc Gugan uses acoustic emission techniques to identify damages in blades [5], Lehmann et. al. [6] applies guided waves and acoustic emission techniques for active and passive monitoring systems. Volkmer uses changes in the frequency spectrum to identify damages or mass increasing by ice-foundation [7]. Also the impedance method was tested on blades [8]. In [9] four different techniques for monitoring of wind turbine blades are compared: transmittance function, resonant comparison, operational deflection shape and wave propagation. The use of optical fiber sensors lately opened new ways for monitoring the blades of OWEPs. First attempts can be found in [10]. An overview of some damage identification methods for wind turbines can be found in [11]. One of the most neglected but expensive part of the energy plant from an SHM point of view is the tower and the foundation. Only few companies and research institutes spend time and money to investigate this, although the impact of the loads from waves or from extreme wind gusts, the ground erosion, etc. are not well-known at the moment. SHM systems for tower and foundation are developed for e.g. by Rolfes et al. [12]. They use the proportionality between the strain and the vibration velocity at certain places on the OWEP for damage detection. Nichols [13] used an attractor-based approach for damage detection of offshore structures under ambient excitation. In [14-16] a combination of the Stochastic Subspace Fault Detection Method and pattern recognition techniques was proposed for damage detection of OWEP. Another often neglected but essential aspect of permanently monitored structures is that the functionality of the monitoring system depends not only on the damage identification method but also on the sensor network installed on the structure. In the ideal case the sensors should have a longer life-time than the structure they have to monitor. In practice, especially under harsh conditions, the sensors themselves have a limited life expectation. The probability that sensor faults occur during an OWEP life time of 20 years is very high. In particular the functionality of strain gauges on sea conditions is guaranteed only for few years. Errors from malfunctioning sensors affect the damage identification result. In some cases the effect of a faulty sensor on the damage identification algorithm can be greater than the effect of a severe damage of the structure.

When a sensor fault occurs, the damage identification algorithm gives a false alarm and the structure will be incorrectly classified as damaged. For this reason, it is necessary to identify the faulty sensors and to exclude them from the damage identification algorithm. For long-time monitoring it is important to integrate sensor fault detection algorithms in the SHM system configuration [3,14,16]. For high levels of the SHM hierarchy like damage prognosis, load identification techniques for different parts of OWEP are necessary. Today many research institutes and companies study different possibilities to identify loads caused by wind and waves on OWEP, e.g. [17-20]. Also guidelines for load monitoring of OWEPs are proposed [21,22]. Kohlmeier et. al. [23] study especially the wave loads on the foundation and tower. Fritzen and Klinkov [24] proposed a robust observer for online load identification. A holistic monitoring of OWEP was performed within IMO-WIND project [25,26], (Integrated Monitoring and Evaluation System for Off-shore Wind Energy Plants) sponsored by the German Ministry of Economics. The consortium consists of six companies and two research establishments: Multibrid (producer of 5MW OWEP), Offshore Wind Technology (design, modeling and calculation), μ-Sen (condition monitoring), IGUS (SHM of blades), Infokom (networks and communications), GermanLoyd Wind (guidelines), Federal Institute for Materials Research and Testing, BAM (measurements and evaluations methods) and the University of Siegen (force identification, SHM of tower, foundation, machine parts and blades including sensor fault detection). CONDITIONS FOR MONITORING OF OWEP To perform online monitoring of OWEPs it is very important to know the influences of different operational and environmental conditions on the dynamical behavior of the plant, whereas the wind velocity and direction, the temperature, the position of the nacelle, the rotation velocity of the blades, the atmospheric conditions (rain) are changing permanently. Sometimes also the boundary conditions are changing (e.g. by ground erosion). For example, the wind velocity, the wind direction and the height of waves affect the excitation level of the blades and structure (e.g. at low wind velocity, only a few numbers of modes are excited). It is also known from bridges that a high wind velocity can cause a change in the eigenfrequencies [27]. The change in the temperature can affect dynamical behavior. Since the structure of the OWEP is not perfectly symmetric along the tower axis (caused by transformers, pumps, etc. which are placed along the tower), the distribution of mass/mass moment of inertia changes due to the orientation of the nacelle, and with them also the dynamics of the system. Since the methods for damage identification and prognosis proposed in the project IMO-WIND deal at the moment mostly with the structure and the foundation, mainly these items will be considered in this paper. DAMAGE IDENTIFICATION OF THE STRUCTURAL PARTS The concept for the permanent SHM of OWEP structure presented in this paper consists of three steps: 1) damage detection under changing operational conditions; 2) sensor fault detection; 3) damage localization. The concept is presented in Figure 2. Damage detection Methods for damage detection under changing operational conditions were developed by Sohn et al. using a combination of AR-ARX (AR models with exogenous inputs) models with Non Linear Principal Component Analysis (NLPCA) [32]. Kullaa applies missing data analysis [33] or factor analysis [34] and non-linear factor analysis [35] to eliminate the environmental effects from damage sensitive features. Yan et. al [36] propose a local PCA for structural damage diagnosis under changing environmental conditions. A very good review of the methods for compensation of environmental conditions can be found in [27]. In this contribution an algorithm for damage detection based on the SSFD method developed in [37] and, alternatively, a method based on the multivariate AR modeling [38] is used. Both methods are designed for output-only systems under the assumption that the unknown excitation is a stationary white noise signal. The basic idea of these methods consists of extracting some features nθ from the incoming measured data and comparing them with the features 0θ of reference data. E.g. for SSFD method the residual errors nζ are calculated from the left singular vector of the baseline Hankelmatrix ( )0θH arranged column-wise in the matrix K and the Hankel matrices themselves from the incoming data (e.g. acceleration measurements).

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Figure 3. Damage localization algorithm [47, 48]

When the change in the system is not caused by changing ambient excitation, changing operational conditions or sensor faults, it will be assumed that a structural damage has occurred which will be localized in the third step of the SHM system. For damage localization a model based algorithm based on the inverse eigensensitivity is applied to the structure, for details see [47] and [48]. The basic idea of the technique is to compare the measured mode shapes and the eigenfrequencies of the OWEP (which requires a modal analysis of structures under ambient excitation) with the mode shapes and the eigenfrequencies from an analytical model of the undamaged OWEP for a specific operational condition. When damage occurs (loss of stiffness by cracks or loose bolts on the tower) the measured mode shapes and eigenfrequencies deviate from the reference data. The position of damage can be localized with the help of the model. It is important that the modal analysis is applied to measured data “collected” under the same excitation and operational conditions. The main components of this algorithm are displayed in Figure 3.

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appropriate set of observer matrices should be found, so that the error between the estimated inputs and states and the real one will converge to zero as the time evolves. It is possible to find these matrices with the help of a linear matrix inequality (LMI) technique. Two assumptions in this method have to be satisfied: the D matrix must have full column rank and the number of sensors must be greater or equal to the number of unknown inputs plus the number of unknown nonlinear terms. Once the observer matrices are calculated, the inputs and states can be reconstructed simultaneously online. The ability of online reconstruction and inherited noise robustness make the SSIE very attractive for external load estimation. RESULTS OF THE INTEGRATED SHM SYSTEM ON OWEP M5000-2 The SHM approaches were applied on measured time data from the prototype OWEP M5000-2 of the company AREVA-Multibrid in Bremerhaven, Germany (see Figure 5, left). The sensor signals are provided by eight accelerometers positioned on the tower as shown in Figure 5, right. The signals were measured simultaneously with a sample rate of 50Hz. The measurement time for one data set was ten minutes. In order to perform long time monitoring 5758 data sets were considered. The first 2016 data sets were continuously measured in November 2007 (first 1008) and March 2008 (next 1008). The remaining 3742 data sets were measured between March and September 2009 (one data set in one hour).

Figure 5. OWEP M5000-2 (left); sensor positions (right)

Since the SHM approaches uses the premise that the excitation of the structure is unknown but normally distributed (output-only system with white noise excitation) it is avoided to apply the algorithms on data measured by strong transient excitation. In order to exclude such undesired data sets from the analysis, at first a multivariate outlier technique based on Mahalanobis-distance with thresholds provided by means of the F-distribution are applied on the measured data [64]. This leads to the fact that only 2669 data sets can be evaluated.

The integrated SHM system evaluates the measured data online via internet and gives the operator information about the health state of the sensors and plant. Additional information about the loads acting on the structure is also extracted from the data. The scheme of the online monitoring system is shown in Figure 6.

Figure 6. Integrated SHM System for OWEP M5000-2

A part of the damage detection results are showed in Figure 7. Figure 7 (left) displays the damage indicators after the training phase. Different colors marked the indicators belongs to different classes of EOC. In Figure 7 (right) the damage indicators of one class obtained during the training phase are compared with indicators from the online phase. It can be seen that the indicators from the online data do not exceed the indicator from the training data, so no alarm will be initialized. Since no damage occurred on the monitored OWEP (M5000-2) until today, the possibility to identify small damages by changing EOC will be exemplified by means of simulations. For this purpose a FEM-model of M5000-2 was used in order to generate “measurement” data sets. The model was stochastically excited at the top of the tower by forces resulting from real measured wind velocities. A further periodical excitation of the tower, the effect of the blade passage was also considered. The simulated positions of the nacelle and the wind directions correspond to the measured one. The system response was “measured” in form of “accelerations” at the degree of freedoms corresponds to the real sensor positions (see also Figure 5, right). The measurement duration and the sample rate corresponds to the real measurement data.

Figure 7. Classes with damage indicators (left) and damage indicators in one class (right)

The total number of simulated data sets was 1109. The damage was introduced in the last 101 data sets. The introduced “damage” is a progressive stiffness reduction (Young’s-modulus) of 1, 5 and 10% of element 59, see Figure 8, left. These damages cannot uniquely related to the shift of the frequency spectra. Also changes of stable

poles obtained by means of operational modal analysis are masked by the effects of changing of environmental and operational conditions.

Figure 8. Simulated damage on Element 59 (left) and results of damage detection for different damage extensions

and number of classes (right)

The damage detection method gives good results using a relatively high number of training data sets and classes. Figure 8 (right) shows the results using 800 training data sets. Here no false alarms occur and also small damage of 1% stiffness reduction could be well identified. A significant reduction of the number of training data sets can cause few false alarms sporadically. This “deficit” can be referred to the high sensitivity of the NSFD algorithm w.r.t. each system change. This effect is a well-known behavior of non model-based algorithms for damage identification: “if an algorithm is sensitive to the damage so it is also sensitive to changing of EOC” [65]. In our case the reduction of the numbers of false alarms can be achieved by the reduction of the number of references (classes). But this apparent improvement reduces also the sensitivity of the method to detect small damages. Since no faults of the channels 65-72 (see Figure 5, right) occur during the measurements, the signal of sensor 3 (channel 67) was artificially substituted in the last 200 data sets by a noise signal with the same statistical properties as the original signal. The results of MI approach shows clearly sensor 3 as faulty in the last 200 data, see Figure 9. For a better exemplification of the results the change of the MI-matrix in the data set 2539 is displayed in Figure 9, right. It can be seen that the MI value between the sensor three and all the other sensors changes significantly.

Figure 9. Results of the sensor fault detection algorithm (left: long time monitoring; right: MI change for

measurement no. 2539)

For validation of the damage localization method by absence of a real damage on the M5000-2 the FE-model in Figure 5, right is used. The modal data used for model-updating and as residuals for localization algorithm is provided in an automatic way by mean of an operational modal analysis using vector autoregressive models [16].

Due to the sparse sensor network on the tower only four bending modes of the structure could be well identified. So the inverse eigensensitivity method was applied by using only these four modes of the structure determined at the four sensor locations in Figure 5. In order to investigate the possibility to identify damages in the tower, the local stiffness of the tower was reduced by 5%, see Figure 10, left. The results in Figure 10 (right) show the correct identified damage position. More complicated seems to identify damages under the water surface, where no sensors are installed and the structure is very stiff. Therefore a stiffness reduction of 5% at the hot spot of the tripod was simulated (Figure 11, left). This damage was also correctly identified, as shown in Figure 11, right.

Figure 10: Damage of the tower (left: damage simulation; right: results of localization)

Figure 11: Damage of the tripod (left: damage simulation; right: results of localization)

The above mentioned methods for SHM were successfully applied on measured time data from a laboratory structure (scaled model of a OWEP) or on the WEP M5000-1, see [3,14,16]. Two sensor types (two 2-D accelerometers and two strain gauges see Figure 12 left) were needed for the estimation of the external loads which are acting on the tower of the WEP. These data was then fed into the observer, based on a reduced modal model which itself was constructed from an FE model (see Figure 12 right).

Figure 12. Load estimation for the prototype WEP

The initial FE model was improved using model-updating techniques, converted into modal coordinates and finally transformed into state space notation. The thick red spots on the model (see Figure 12 right) represent the locations of the accelerometer (top) and strain gauge (bottom), respectively. Load estimation was then done for the section where the nacelle is connected to the tower. This section is chosen intentionally and includes all possible forces that the tower should subtend (external wind forces; static nacelle orientation, blades dynamics; mass unbalance; etc.). For the qualitative validation of the estimated load one of the working states (see Figure 13 right) of the WEP M5000-2 that was close to the ideal operational conditions has been chosen see data sheet [66]: a) wind comes almost orthogonal to the rotor area, with a nearly constant wind speed of approximately 12 m/s; b) the blades have a relatively small pitch angle. They allow using the Betz theory for validation of the estimated load. According to Betz the wind load which is applied to the rotor area can be calculated as:

212Betz windF c V Aρ= (5)

where, c is an aerodynamic coefficient, Vwind is the wind speed, ρ is the air density and A is a rotor area accordingly. The wind is assumed to blow orthogonal to the rotor area. The estimated and Betz forces are depicted in Figure 13 for a time duration of 25 minutes and the above mentioned operational conditions

Figure 13. Estimated external force for M5000-2 (left), operational conditions (right)

Figure 13 shows a certain difference between estimated and Betz force especially in the region where the wind velocity is increasing. This is due to Betz theory; see Eq. 5, where the Betz force follows the wind velocity behavior for a fixed coefficient c without consideration of pitch angles (see for comparison the wind velocity in Figure 13 right in the time interval 0-1500 seconds, wind velocity increases as a time evolves). As a result the Betz force cannot be used solely for the validation, instead one needs also to take into consideration the pitch angle of the rotor blades. A closer look on Figure 13 left shows that the estimated force is very close to the Betz force in the regions where the pitch is close to zero (~100 and ~1300 seconds) and becomes smaller as rotor blades are pitched (see pitch and wind velocity in Figure 13 right in time interval 0-1500 seconds). CONCLUSIONS For the last decades practice has proved that the wind energy industry is one of the leading and promising among the renewable energy manufactures. Additionally the industrial trend moves to a bigger construction of WEP's and their global shift to offshore regions due to higher wind speeds. Experience show that the cost of operation and maintenance raise dramatically with the shift into offshore zone compared to onshore WEP's. Especially, vessel transportation of the damaged components/spare parts or inspection teams is cost demanding and challenging in case of stormy weather.

Therefore, SHM and also CBM systems with the ability of online remote forecasting of critical failure situations of components is an indispensable integral part of new WEP concepts. The integration of the SHM system into the WEP will not only save cost but also provide information for future design improvement of these items. Presently, research teams and companies attempt to develop such monitoring systems or independent monitoring components for gear and bearings, blades and the supporting structure. The IMO-WIND project forming a network of WEP manufactures and research institutes illustrates the importance of multidisciplinary teams to develop SHM system for the offshore WEPs and to be successful with system integration. ACKNOWLEDGEMENT The authors are grateful to the German Ministry of Economics and the companies Multibrid, OWT, μ-Sen, IGUS, Infokom and GL Wind for the financial support of the IMO-WIND project by funding of BMWi (grant no. 16INO327). We are also grateful to the Federal Institute for Materials Research and Testing (BAM VII-2) and to the company AREVA-Multibrid for providing the measurement data. REFERENCES 1 Delft University Wind Energy Research Institute (Duwind), 2001, “Concerted Action on Offshore Wind Energy in

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