Analysis of the influence of climate change on the fatigue lifetime of offshore wind turbines using imprecise probabilities

When discussing the connection of wind energy and climate change, normally, the potential of wind energy to reduce green house gas emissions is emphasised. Hence, effects of wind energy on climate change are analysed. However, what about the other direction? What is the impact of climate change on wind energy? Recently, the effect of a reversal in global terrestrial stilling,that is, an increase in global wind speeds in the last decade, on the wind energy production has been analysed. Certainly, knowledge about potential changes in energy production is essential to plan future energy supply. Nonetheless, at least similarly important is the effect on loads acting on wind turbines. Increasing loads due to higher wind speeds might reduce wind turbine lifetimes and yield higher costs. Moreover, especially for already existing turbines, it might even affect the structural reliability. Since the impact of climate change on wind turbine loads is largely unknown, it is studied in this work in more detail. For this purpose, different existing models for predicted changes in wind speed and air temperature and their uncertainties are used to forecast the environmental conditions an exemplary offshore wind turbine is exposed to. Subsequently, for this turbine, the lifetime fatigue damages are calculated for different prediction models. It is shown that the expected changes in lifetime fatigue damages are present but relatively small compared to other uncertainties in the fatigue damage calculation

Probabilistic temporal extrapolation of fatigue damage of offshore wind turbine substructures based on strain measurements

Substructures of offshore wind turbines are becoming older and beginning to reach their design lifetimes. Hence, lifetime extensions for offshore wind turbines are becoming not only an interesting research topic but also a relevant option for industry. To make well-founded decisions on possible lifetime extensions, precise fatigue damage predictions are required. In contrast to the design phase, fatigue damage predictions can be based not only on aeroelastic simulations but also on strain measurements. Nonetheless, strain-measurement-based fatigue damage assessments for lifetime extensions have been rarely conducted so far. Simulation-based approaches are much more common, although current standards explicitly recommend the use of measurement-based approaches as well. For measurement-based approaches, the main challenge is that strain data are limited. This means that measurements are only available for a limited period and only at some specific hotspot locations. Hence, spatial and temporal extrapolations are required. Available procedures are not yet standardised and in most cases not validated. This work focusses on extrapolations in time. Several methods for the extrapolation of fatigue damage are assessed. The methods are intended to extrapolate fatigue damage calculated for a limited time period using strain measurement data to a longer time period or another time period, where no such data are available. This could be, for example, a future period, a period prior to the installation of strain gauges or a period after some sensors have failed. The methods are validated using several years of strain measurement data from the German offshore wind farm Alpha Ventus. The performance and user-friendliness of the various methods are compared. It is shown that fatigue damage can be predicted accurately and reliably for periods where no strain data are available. Best results are achieved if wind speed correlations are taken into account by applying a binning approach and if a least some winter months of strain data are available

Assessment of a standard ULS design procedure for offshore wind turbine sub-structures

Sub-structures of offshore wind turbines are designed according to several design load cases (DLCs) that cover various fatigue (FLS) and ultimate limit states (ULS). The required DLCs are given in the current standards, and are supposed, on the one hand, to cover accurately all significant load conditions to guarantee reliability. On the other hand, they should include only necessary conditions to keep computing times manageable. For ULS conditions, the current work addresses the question whether the current design practice is, firstly, sufficient, and secondly, sensible concerning the computing time by only including necessary DLCs. To address this topic, data of five years of normal operation, simulated using a probabilistic approach, is used to extrapolate 20-year ULS loads (comparable to a probabilistic version of DLC 1.1 for substructures). These ULS values are compared to several deterministic DLCs required by current standards. Results show that probabilistic, extrapolated ULS values are fairly high and exceed standard DLC loads. Hence, the current design practice might not always be conservative. Especially, the benefit of an additional DLC for wave peak periods close to the eigenfrequency of the sub-structure is indicated

Raw Data Is All You Need: Virtual Axle Detector with Enhanced Receptive Field

Rising maintenance costs of ageing infrastructure necessitate innovative monitoring techniques. This paper presents a new approach for axle detection, enabling real-time application of Bridge Weigh-In-Motion (BWIM) systems without dedicated axle detectors. The proposed method adapts the Virtual Axle Detector (VAD) model to handle raw acceleration data, which allows the receptive field to be increased. The proposed Virtual Axle Detector with Enhanced Receptive field (VADER) improves the $F_1$ score by 73\% and spatial accuracy by 39\%, while cutting computational and memory costs by 99\% compared to the state-of-the-art VAD. VADER reaches a $F_1$ score of 99.4\% and a spatial error of 4.13~cm when using a representative training set and functional sensors. We also introduce a novel receptive field (RF) rule for an object-size driven design of Convolutional Neural Network (CNN) architectures. Based on this rule, our results suggest that models using raw data could achieve better performance than those using spectrograms, offering a compelling reason to consider raw data as input

MOGPS Supplementary Material

Additional analytical test functions optimised using Multi-Objective Global Pattern Search

Multi-Objective Global Pattern Search: Effective numerical optimisation in structural dynamics

With this work, a novel derivative-free multi-objective optimisation approach for solving engineering problems is presented. State-of-the-art algorithms usually require numerical experimentation in order to tune the algorithm’s multiple parameters to a specific optimisation problem. This issue is effectively tackled by the presented deterministic method which has only a single parameter. The most popular multi-objective optimisation algorithms are based on pseudo-random numbers and need several parameters to adjust the associated probability distributions. Deterministic methods can overcome this issue but have not attracted much research interest in the past decades and are thus seldom applied in practice. The proposed multi-objective algorithm is an extension of the previously introduced deterministic single-objective Global Pattern Search algorithm. It achieves a thorough recovery of the Pareto frontier by tracking a predefined number of non-dominated samples during the optimisation run. To assess the numerical efficiency of the proposed method, it is compared to the well-established NSGA2 algorithm. Convergence is demonstrated and the numerical performance of the proposed optimiser is discussed on the basis of several analytic test functions. Finally, the optimiser is applied to two structural dynamics problems: transfer function estimation and finite element model updating. The introduced algorithm performs well on test functions and robustly converges on the considered practical engineering problems. Hence, this deterministic algorithm can be a viable and beneficial alternative to random-number-based approaches in multi-objective engineering optimisation

Validated extrapolation of measured damage within an offshore wind farm using instrumented fleet leaders

As the older wind farms are slowly reaching their design lifetime, topics like fatigue and lifetime assessment gain importance. To decide on a possible lifetime extension of the turbine and its foundation, an accurate fatigue assessment for every wind turbine in the farm is needed. As the installation of specific sensors needed for a fatigue assessment is too time consuming and costly, the "Fleet Leader Concept" is applied and validated in this paper. Here, a few turbines are instrumented and a fatigue assessment based on rainflow counting and Miner's rule can be performed. For a farm-wide fatigue assessment, the obtained damage is extrapolated towards the other turbines. Sample based bootstrapping is performed to introduce an uncertainty on the results. A successful extrapolation was obtained for in-field measurements at an older offshore wind farm. In general, relative errors of less than 5% on damage were found. © 2020 Published under licence by IOP Publishing Ltd

Validation of an FE model updating procedure for damage assessment using a modular laboratory experiment with a reversible damage mechanism

Systematic validation of a deterministic finite element (FE) model updating procedure for damage assessment using a self-developed modular laboratory experiment. The measurement data is made available in open-access form

Data-driven vibration prognosis using multiple-input finite impulse response filters and application to railway-induced vibration of timber buildings

With this paper, we present a vibration prognosis method based on finite impulse responses. The impulse responses are identified using measurement data from an existing building and consider a multiple-input/multiple-output topology. Vibration prognosis in urban buildings is becoming increasingly important, since more and more buildings are being constructed close to urban infrastructure. Combined with modern and ecological choices of building materials and the low vibration levels required by current standards, serviceability in terms of structural dynamics becomes an issue. Sources of vibration in urban settings include railway and metro lines as well as road traffic. This work focuses on a method especially suited to the three- dimensional vibration state encountered in modern timber buildings. Under the assumption of linear time-invariant structural dynamic behaviour, we develop a time- domain identification approach. The novelties of this contribution lie in the formulation of a numerically efficient method to identify multiple-input finite impulse response filters and its application to measurement data of a timber building. We validate this data-driven prognosis method using measurement data from a building constructed from cross-laminated timber, considering the three-dimensional vibration behaviour. The accuracy and limitations are assessed using railway-induced vibrations as a typical source of disturbance by infrastructure. We show that vibration data from the foundation can be used for effective prognosis of the top floor slabs considering train types not included in the identification data set. Based on the prognosis method, a virtual sensor concept for long-term monitoring is presented

Zur Realisierung von Mega-Windturbinen auf See : Sonderforschungsbereich entwickelt digitalen Zwilling

[no abstract available