SCADA-based neural network thrust load model for fatigue assessment: cross validation with in-situ measurements

Santos, Francisco de Nolasco; Noppe, Nymfa; Weijtjens, Wout; Devriendt, Christof

doi:10.1088/1742-6596/1618/2/022020

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Because we are dealing with 10‐min data, it is very difficult (if not impossible) to retrieve the position of the event within the time window. The alternative would consist in full time series reconstruction, but this has been seen to produce higher errors further along the line 71 . One possible way to circumvent this issue with 10‐min signals would be to increase the statistical information of the signal, by including metrics sensitive to peak position, such as skewness and kurtosis (spectral moments).…”

Section: Resultsmentioning

confidence: 99%

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

de N Santos,

Noppe,

Weijtjens

et al. 2024

Wind Energy

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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 . 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.

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Section: Resultsmentioning

confidence: 99%

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

de N Santos,

Noppe,

Weijtjens

et al. 2024

Wind Energy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because we are dealing with ten-minute data, it is very difficult (if not impossible) to retrieve the position of the event within the time window. The alternative would consist in full timeseries reconstruction but this has been seen to produce higher errors further along the line 71 . One possible way to circumvent this issue with tenminute signals would be to increase the statistical information of the signal, by including metrics sensitive to peak position, such as skewness and kurtosis (spectral moments).…”

Section: High Loadsmentioning

confidence: 99%

Farm-wide fatigue loads estimation: a data-driven approach

Noppe,

Weijtjens

et al. 2023

Preprint

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Fatigue has become a major consideration factor in modern offshore wind farms as optimized design codes and a lack of lifetime reserve have 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 tackle 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 ten-minute SCADA statistics and three strain gauge-instrumented ’fleet-leaders’ we discuss the value of two distinct approaches: use of 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 is missing, then its fatigue loads should be modelled as under idling. With this knowledge at hand, we analyzed the errors of the trained model over a period of nine 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 ten-minute statistics, but mitigation strategies have been identified. Finally, the farm-wide results are presented on fatigue load estimation, which allowed to identify outliers, whose behaviour 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.

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“…Following the methodology prescribed in the previous section, one must first exhibit the results for the training, validating and cross-validating of the artificial neural network that estimates the thrust load. The full results and discussion can be found in d N Santos et al (2020a). The high-frequency SCADA data used to train consisted in wind speed (m s −1 ), rotor speed (cps), mean pitch ( • ), nacelle orientation ( • ) and actual active power (kW) from 12 d, carefully selected as to be statistically representative of all operating conditions, namely parked, run-up and full load.…”

Section: Thrust Load Modelmentioning

confidence: 99%

Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups

et al. 2022

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Abstract. The sustained development over the past decades of the offshore wind industry has seen older wind farms beginning to reach their design lifetime. This has led to a greater interest in wind turbine fatigue, the remaining useful lifetime and lifetime extensions. In an attempt to quantify the progression of fatigue life for offshore wind turbines, also referred to as a fatigue assessment, structural health monitoring (SHM) appears as a valuable contribution. Accurate information from a SHM system can enable informed decisions regarding lifetime extensions. Unfortunately direct measurement of fatigue loads typically revolves around the use of strain gauges, and the installation of strain gauges on all turbines of a given farm is generally not considered economically feasible. However, when we consider that great numbers of data, such as supervisory control and data acquisition (SCADA) and accelerometer data (of cheaper installation than strain gauges), are already being captured, these data might be used to circumvent the lack of direct measurements. It is then highly relevant to know what is the minimal sensor instrumentation required for a proper fatigue assessment. In order to determine this minimal instrumentation, a data-driven methodology is developed for real-world jacket-foundation offshore wind turbines (OWTs). In the current study the availability of high-frequency SCADA (1 Hz) and acceleration data (>1 Hz) as well as regular 10 min SCADA is taken as the starting point. Along these measured values, the current work also investigates the inclusion of an estimate of the quasi-static thrust load using the 1 s SCADA using an artificial neural network (ANN). After data collection all data are transformed to features on a 10 min interval (feature generation). When considering all possible variations a total of 430 features was obtained. To reduce the dimensionality of the problem this work performs a comparative analysis of feature selection algorithms. The features selected by each method are compared and related to the sensors to decide on the most cost-effective instrumentation of the OWT. The variables chosen by the best-performing feature selection algorithm then serve as the input for a second ANN, which estimates the tower fore–aft (FA) bending moment damage equivalent loads (DELs), a valuable metric closely related to fatigue. This approach can then be understood as a two-tier model: the first tier concerns itself with engineering and processing 10 min features, which will serve as an input for the second tier that estimates the DELs. It is this two-tier methodology that is used to assess the performance of eight realistic instrumentation setups (ranging from 10 min SCADA to 1 s SCADA, thrust load and dedicated tower SHM accelerometers). Amongst other findings, it was seen that accelerations are essential for the model's generalization. The best-performing instrumentation setup is looked at in greater depth, with validation results of the tower FA DEL ANN model showing an accuracy of around 1 % (MAE) for the training turbine and below 3 % for other turbines, with a slight underprediction of fatigue rates. Finally, the ANN DEL estimation model – based on two intermediate instrumentation setups (combinations of 1 s SCADA, thrust load, low quality accelerations) – is employed in a farm-wide setting, and the probable causes for outlier behaviour are investigated.

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SCADA-based neural network thrust load model for fatigue assessment: cross validation with in-situ measurements

Cited by 8 publications

References 7 publications

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

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

Farm-wide fatigue loads estimation: a data-driven approach

Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups

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