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

Farm‐wide interface fatigue loads estimation: A data‐driven approach based on accelerometers

Abstract Fatigue has become a major consideration factor in modern offshore wind farms as optimized design codes, and a lack of lifetime reserve has made continuous fatigue life monitoring become an operational concern. In this contribution, we discuss a data‐driven methodology for farm‐wide tower‐transition piece fatigue load estimation. We specifically debate the employment of this methodology in a real‐world farm‐wide setting and the implications of continuous monitoring. With reliable nacelle‐installed accelerometer data at all locations, along with the customary 10‐min supervisory control and data acquisition (SCADA) statistics and three strain gauge‐instrumented 'fleet‐leaders', we discuss the value of two distinct approaches: use of either fleet‐leader or population‐based data for training a physics‐guided neural network model with a built‐in conservative bias, with the latter taking precedence. In the context of continuous monitoring, we touch on the importance of data imputation, working under the assumption that if data are missing, then its fatigue loads should be modeled as under idling. With this knowledge at hand, we analyzed the errors of the trained model over a period of 9 months, with monthly accumulated errors always kept below ±5%. A particular focus was given to performance under high loads, where higher errors were found. The cause for this error was identified as being inherent to the use of 10‐min statistics, but mitigation strategies have been identified. Finally, the farm‐wide results are presented on fatigue load estimation, which allowed to identify outliers, whose behavior we correlated with the operational conditions. Finally, the continuous data‐driven, population‐based approach here presented can serve as a springboard for further lifetime‐based decision‐making

Data-driven multivariate power curve modeling of offshore wind turbines

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