1 research outputs found
Farmâwide interface fatigue loads estimation: A dataâdriven 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 dataâdriven methodology for farmâwide towerâtransition piece fatigue load estimation. We specifically debate the employment of this methodology in a realâworld farmâwide setting and the implications of continuous monitoring. With reliable nacelleâinstalled accelerometer data at all locations, along with the customary 10âmin supervisory control and data acquisition (SCADA) statistics and three strain gaugeâinstrumented 'fleetâleaders', we discuss the value of two distinct approaches: use of either fleetâleader or populationâbased data for training a physicsâguided neural network model with a builtâin 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 10âmin statistics, but mitigation strategies have been identified. Finally, the farmâwide results are presented on fatigue load estimation, which allowed to identify outliers, whose behavior we correlated with the operational conditions. Finally, the continuous dataâdriven, populationâbased approach here presented can serve as a springboard for further lifetimeâbased decisionâmaking