Abstract
Structural Health Monitoring (SHM) and Condition Monitoring (CM) Systems are currently utilised to collect data from offshore wind turbines (OWTs), to enhance the accurate estimation of their operational performance. However, industry accepted practices for effectively managing the information that these systems provide have not been widely established yet. This paper presents a four-step methodological framework for the effective data management of SHM systems of OWTs and illustrates its applicability in real-time continuous data collected from three operational units, with the aim of utilising more complete and accurate datasets for fatigue life assessment of support structures. Firstly, a time-efficient synchronisation method that enables the continuous monitoring of these systems is presented, followed by a novel approach to noise cleansing and the posterior missing data imputation (MDI). By the implementation of these techniques those data-points containing excessive noise are removed from the dataset (Step 2), advanced numerical tools are employed to regenerate missing data (Step 3) and fatigue is estimated for the results of these two methodologies (Step 4). Results show that after cleansing, missing data can be imputed with an average absolute error of 2.1%, while this error is kept within the [+ 15.2%−11.0%] range in 95% of cases. Furthermore, only 0.15% of the imputed data fell outside the noise thresholds. Fatigue is found to be underestimated both, when data cleansing does not take place and when it takes place but MDI does not. This makes this novel methodology an enhancement to conventional structural integrity assessment techniques that do not employ continuous datasets in their analyses.
Highlights
Structural Health Monitoring Systems (SHMS) have become relevant in the last decade for the operational management of Offshore Wind Turbines (OWTs) due to their damage detection and continuous fatigue life assessment capabilities
While in the past SHMS were installed as a way to abide by the German regulations (imposing a 10% of assets instrumented across an offshore wind farm (OWF)) and not exploited to their full potential, nowadays operators have realized how these technologies could result in an increase in electricity production and thereby a reduction in Levelized Cost of Energy (LCoE) (Ioannou et al, 2018; Myhr et al, 2014)
The reason is that the noise is picked up in the algorithm and reproduced, making the cumulative effect to considerably increase the overall fatigue of the structure
Summary
Structural Health Monitoring Systems (SHMS) have become relevant in the last decade for the operational management of Offshore Wind Turbines (OWTs) due to their damage detection and continuous fatigue life assessment capabilities. Many researchers from the SHM community have developed an extensive amount of methods based on a variety of physically interpretable structural features (Hansen et al, 2017) At this point in time there is no widely accepted practice with respect to the specification of monitoring systems, as industry is still exploring WTs' potential, making every wind farm different in terms of technologies implemented, number and location of the sensors, redundancies, etc. Most of these fatigue assessment methods rely on collected data from either accelerometers, strain gauges or the combination of both from selected instrumented units (Luengo and Kolios, 2015; Martinez-Luengo et al, 2016). Numerous authors have carried out different ways of analysing SHMS’ data – for example, a vibration-based damage localization and quantification method, based on natural frequencies and mode shapes extracted by means of Operational Modal Analysis (OMA) combined with Finite Element Analysis (FEA) of the test structure (Hansen et al, 2017)
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