Wout Weijtjens

Operational modal parameter estimation of MIMO systems using transmissibility functions

Operational modal parameter estimation (OMA) techniques perform system identification without or with only limited knowledge of the operational inputs acting on the system. However, most of the current operational identification techniques impose multiple conditions on the spectral content of the unknown inputs. As a consequence, modeling errors occur if these assumptions are not met. Therefore, there is a general interest in operational identification techniques that can operate independent of the unknown input spectra. This paper introduces poly-reference Transmissibility based Operational Modal Analysis (pTOMA). pTOMA uses parametrically estimated transmissibility functions associated with different loading conditions to obtain the system eigenvalues and eigenvectors using output-only data. Unlike most OMA techniques no strong assumptions are necessary considering the input spectrum. The method is therefore able to correctly identify the system parameters while the excitation may contain (varying) harmonics or strong coloration. A framework to use pTOMA is formulated, the algorithm is introduced and the claimed properties are illustrated by means of a numerical experiment.

Structural health monitoring of offshore wind turbines using automated operational modal analysis

This article will present and discuss the approach and the first results of a long-term dynamic monitoring campaign on an offshore wind turbine in the Belgian North Sea. It focuses on the vibration levels and modal parameters of the fundamental modes of the support structure. These parameters are crucial to minimize the operation and maintenance costs and to extend the lifetime of offshore wind turbine structure and mechanical systems. In order to perform a proper continuous monitoring during operation, a fast and reliable solution, applicable on an industrial scale, has been developed. It will be shown that the use of appropriate vibration measurement equipment together with state-of-the art operational modal analysis techniques can provide accurate estimates of natural frequencies, damping ratios, and mode shapes of offshore wind turbines. The identification methods have been automated and their reliability has been improved, so that the system can track small changes in the dynamic behavior of offshore wind turbines. The advanced modal analysis tools used in this application include the poly-reference least squares complex frequency-domain estimator, commercially known as PolyMAX, and the covariance-driven stochastic subspace identification method. The implemented processing strategy will be demonstrated on data continuously collected during 2 weeks, while the wind turbine was idling or parked.

Foundation structural health monitoring of an offshore wind turbine—a full-scale case study

In this contribution, first, the results in the development of a structural health monitoring approach for the foundations of an offshore wind turbine based on its resonance frequencies will be presented. Key problems are the operational and environmental variability of the resonance frequencies of the turbine that potentially conceal any structural change. This article uses a (non-)linear regression model to compensate for the environmental variations. An operational case-by-case monitoring strategy is suggested to cope with the dynamic variability between different operational cases of the turbine. Real-life data obtained from an offshore turbine on a monopile foundation are used to validate the presented strategy and to demonstrate the performance of the presented approach. First, the results indicate an overall stiffening of the investigated structure.

Full load estimation of an offshore wind turbine based on SCADA and accelerometer data

As offshore wind farms (OWFs) grow older, the optimal use of the actual fatigue lifetime of an offshore wind turbine (OWT) and predominantly its foundation will get more important. In case of OWTs, both quasi-static wind/thrust loads and dynamic loads, as induced by turbulence, waves and the turbines dynamics, contribute to its fatigue life progression. To estimate the remaining useful life of an OWT, the stresses acting on the fatigue critical locations within the structure should be monitored continuously. Unfortunately, in case of the most common monopile foundations these locations are often situated below sea-level and near the mud line and thus difficult or even impossible to access for existing OWTs. Actual strain measurements taken at accessible locations above the sea level show a correlation between thrust load and several SCADA parameters. Therefore a model is created to estimate the thrust load using SCADA data and strain measurements. Afterwards the thrust load acting on the OWT is estimated using the created model and SCADA data only. From this model the quasi static loads on the foundation can be estimated over the lifetime of the OWT. To estimate the contribution of the dynamic loads a modal decomposition and expansion based virtual sensing technique is applied. This method only uses acceleration measurements recorded at accessible locations on the tower. Superimposing both contributions leads to a so-called multi-band virtual sensing. The result is a method that allows to estimate the strain history at any location on the foundation and thus the full load, being a combination of both quasi-static and dynamic loads, acting on the entire structure. This approach is validated using data from an operating Belgian OWF. An initial good match between measured and predicted strains for a short period of time proofs the concept.

Full-Field Strain Prediction Applied to an Offshore Wind Turbine

Fatigue life is a design driver for the foundations of offshore wind turbines (OWT’s). A full-scope structural health monitoring strategy for OWT’s needs to consider the continuous monitoring of the consumption of fatigue life as an essential part. To do so, the actual stress distribution along the entire length of the structure and predominantly at the fatigue hotspots needs to be known. However installation of strain sensors at these hotspots is not always feasible since these hotspots are mainly situated beneath the water level (e.g., mudline). In practice this implies the installation of strain gauges on the monopile prior to pile driving and difficulty in maintaining these submerged sensors throughout the operational life of the turbine. Therefore, an effective and robust implemented technique using the more reliable accelerometers and very limited strain sensors at few easily accessible locations integrated within a new analytical structural dynamic approach is preferred. In this paper, a novel multi-band implementation of the well-known modal expansion approach, a.k.a. full-field strain prediction, is introduced. This technique utilizes the limited set of response data derived during a monitoring campaign and a calibrated Finite Element Model (FEM) to reconstruct the full field response of the structure. The obtained virtual responses are compared with measurements from an ongoing measurement campaign on an offshore wind turbine.

Long-term Prediction of Dynamic Responses on an Offshore Wind Turbine Using a Virtual Sensor Approach

Since fatigue life is a design driver for the foundations, the continuous monitoring for life-time assessment of an offshore wind turbine during its wide range of operational states can serve as a valuable tool for maintenance, end-of-life decisions and feedback into design for optimization of future substructures. For the offshore wind turbine, though, practical limitations prohibit to mount sensors at stress (and fatigue) hotspots. E.g. for a monopile foundation, the most popular design, the stress hot spot is at the mudline below the water level. Installing a measurement system at the mudline is unfavourable in terms of cost and maintenance. This limitation is overcome by reconstructing the full-field response of the structure based on the limited number of accelerometers and a calibrated Finite Element Model of the system. A reduced-order model that exploits the limited information obtained by the acceleration sensor data and adaptively incorporates them to permit adaptation to system changes is utilised for optimal generation of virtual dynamic strains. The model uses a multi band modal decomposition and expansion approach for reconstructing the responses at all degrees of freedom of the finite element model. The paper will demonstrate the possibility to estimate dynamic strains from acceleration measurements based on the aforementioned methodology. These virtual dynamic strains will then be evaluated and validated based on long term actual strain measurements obtained from a monitoring campaign on an offshore wind turbine on a monopile foundation. This new structural health monitoring approach has the ability to interrogate an entire structure and accurately assess fatigue life consumption and remaining useful life at the true fatigue hot spots. doi: 10.12783/SHM2015/346

Prediction of dynamic strains on a monopile offshore wind turbine using virtual sensors

The monitoring of the condition of the offshore wind turbine during its operational states offers the possibility of performing accurate assessments of the remaining life-time as well as supporting maintenance decisions during its entire life. The efficacy of structural monitoring in the case of the offshore wind turbine, though, is undermined by the practical limitations connected to the measurement system in terms of cost, weight and feasibility of sensor mounting (e.g. at muddline level 30m below the water level). This limitation is overcome by reconstructing the full-field response of the structure based on the limited number of measured accelerations and a calibrated Finite Element Model of the system. A modal decomposition and expansion approach is used for reconstructing the responses at all degrees of freedom of the finite element model. The paper will demonstrate the possibility to predict dynamic strains from acceleration measurements based on the aforementioned methodology. These virtual dynamic strains will then be evaluated and validated based on actual strain measurements obtained from a monitoring campaign on an offshore Vestas V90 3 MW wind turbine on a monopile foundation.

Evaluating Different Automated Operational Modal Analysis Techniques for the Continuous Monitoring of Offshore Wind Turbines

This paper will evaluate different automated operational modal analysis techniques for the continuous monitoring of offshore wind turbines. The experimental data has been obtained during a long-term monitoring campaign on an offshore wind turbine in the Belgian North Sea. State-of-the art operational modal analysis techniques and the use of appropriate vibration measurement equipment can provide accurate estimates of natural frequencies, damping ratios and mode shapes of offshore wind turbines. To allow a proper continuous monitoring the methods have been automated and their reliability improved. The advanced modal analysis tools, which will be used, include the poly-reference Least Squares Complex Frequency-domain estimator (pLSCF), commercially known as PolyMAX, the polyreference maximum likelihood estimator (pMLE), and the frequency-domain subspace identification (FSSI) technique. The robustness of these estimators with respect to a possible change in the implementation options that could be defined by the user (e.g. type of polynomial coefficients used, parameter constraint used…) will be investigated. In order to improve the automation of the techniques, an alternative representation for the stabilization charts as well as robust cluster algorithms will be presented.

Online Input and State Estimation in Structural Dynamics

This paper presents two applications of joint input-state estimation in structural dynamics. The considered joint input-state estimation algorithm relies upon a limited set of response measurements and a system model, and can be applied for online input and state estimation on structures. In the first case, the algorithm is applied for force identification on a footbridge. The second case shows an application where strains in the tower of an offshore monopile wind turbine are estimated. In both cases, real measured data obtained from in situ measurements are used for the estimation. The dynamic system model, used in the estimation, is for both case studies obtained from a finite element model of the structure. The quality of the force and response estimates is assessed by comparison with the corresponding measured quantities.

Operational Modal Analysis in the Presence of Harmonic Excitations: A Review

Over the past years the use of Operational Modal Analysis (OMA) for Structural health monitoring has become more and more widespread. Such a methodology would also be relevant to wind farm owners that want to monitor the integrity of their turbines’ foundations.