A surrogate model approach for associating wind farm load variations with turbine failures

Abstract

Abstract. In order to ensure structural reliability, wind turbine design is typically based on the assumption of gradual degradation of material properties (fatigue loading). Nevertheless, the relation between the wake-induced load exposure of turbines and the reliability of their major components has not been sufficiently well defined and demonstrated. This study suggests a methodology that makes it possible to correlate loads with reliability of turbines in wind farms in a computationally efficient way by combining physical modeling with machine learning. It can be used for estimating the current health state of a turbine and enables a more precise prediction of the “load budget”, i.e., the effect of load-induced degradation and faults on the operating costs of wind farms. The suggested approach is demonstrated on an offshore wind farm for comparing performance, loads and lifetime estimations against recorded main bearing failures from maintenance reports. The validation of the estimated power against the 10 min supervisory control and data acquisition (SCADA) power signals shows that the surrogate model is able to capture the power performance relatively well with a 1.5 % average error in the prediction of the annual energy production (AEP). It is found that turbines positioned at the border of the wind farm with a higher expected AEP are estimated to experience earlier main bearing failures. However, a clear connection between the load estimations and failure observations could not be confirmed in this study. Finally, the analysis stresses that more failure data are required in future work to enable statistically significant associations of the observed main bearing lifetimes with load exposures across the wind farm and to validate and generalize the suggested approach and its associated findings.

Using Transfer Learning to Build Physics-Informed Machine Learning Models for Improved Wind Farm Monitoring

“…In general, the implications of augmenting SCADA-based monitoring models with the simulated data depends on various factors during the modeling process: Firstly, the simulation settings should be selected in order to model the turbine as representative as possible under the given environmental conditions. Here, uncertainties of the simulations might arise from the variable space definitions, environmental input selection and the wake model used [15]. Secondly, the aeroelastic model itself inhibits strengths and weaknesses in modeling the physical system of the turbine physics.…”

“…The choice of input variables and their probability distributions is based on the intention to cover a wide range of environmental conditions that are representative for the wind farm used in this study. Further details about the input variable distributions and their boundary functions can be found in a previous study by the authors in which the database has been created [15].…”

“…This variability causes uncertainty in the turbine performance. However, the uncertainty for the performance estimation through the variability of the wind is reduced by using a large Monte Carlo sample [15]. The dynamic wake meandering (DWM) model [19] is used for simulating the wake effects from multiple wake sources N rows .…”

“…Finally, the time series simulations S t (X) are postprocessed in order to calculate the 10 min statistics. The interested reader can find further details about the method and specific simulation set up in [15]. The database is openly available in https://doi.org/10.1 1583/DTU.12245978 (accessed on 10 January 2022).…”

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

“…[110] showing also the location of the damaged bar by red circle, and (b) the flowchart summarizing the damage diagnose strategy proposed by [110]. Schröder et al proposed a methodology to correlate loads with turbine reliability in wind farms by combining physical modeling and an MLP network [112]. The approach was demonstrated on an offshore wind farm, comparing performance, loads, and lifetime estimations against recorded main bearing failures from maintenance reports.…”