Towards a Fleetwide Data-Driven Lifetime Assessment Methodology of Offshore Wind Support Structures Based on SCADA and SHM Data

Santos, Francisco de Nolasco; Robbelein, Koen; D’Antuono, Pietro; Noppe, Nymfa; Weijtjens, Wout; Devriendt, Christof

doi:10.1007/978-3-031-07254-3_13

Cited by 4 publications

(10 citation statements)

References 17 publications

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“…40 And (ii), due to data availability (or better said, the lack thereof), specifically accelerometer installation, no farm-wide employment of the methodologies developed for fatigue load estimation on the substructure on one or a couple of turbines has been presented for a real-world case involving a full farm. Point (i) has been addressed in de N Santos et al 5 (an extension of de N Santos et al 26 ), where there is a rescaling and accumulation of fatigue loads and damages undertook for two real-world turbines over 2 years. Thereafter, the necessity to accurately predict fatigue loads on a longterm level led to the introduction of a physics-guided ML approach (Φ-ML) in.…”

Section: Fatigue Monitoringmentioning

confidence: 99%

“…However, one does not need to restrict oneself to a monthly accumulation. In Figure 13, we present the daily DEL accumulation for all turbines of the farm (presented as DEL 5 ). This figure can be seen as the culmination of this contribution, as it shows the accumulated damage equivalent loads on a daily basis for all turbines within a real-world farm based on SCADA and reliable acceleration measurements.…”

Section: Farm-widementioning

confidence: 99%

“…In some older offshore wind farms, real-world observations through the use of structural health monitoring (SHM) appear to have identified an additional structural reserve, suggesting that fatigue life consumption was less than as per design. 5 Conversely, this structural reserve seems to be absent in more recent projects which employ updated design procedures which allow a more accurate description of structural behavior (e.g., PISA 6 ). This has meant that operators of newer farms, instead of looking for a possible lifetime extension, need to keep tabs on the fatigue progression of their assets to ensure that the intended project life can be reached.…”

Section: Introduction 1| Motivationmentioning

confidence: 99%

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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: Fatigue Monitoringmentioning

confidence: 99%

Section: Farm-widementioning

confidence: 99%

Section: Introduction 1| Motivationmentioning

confidence: 99%

See 1 more Smart Citation

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

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“…This updated model-driven simulations have been subject of research and industrial applications [3][4]. While the use of monitoring data is also recommended in current guidelines, the actual use-cases of data-driven fatigue assessments with load monitoring data are mainly found in a research context [5] [15]. The present work aims to investigate the potential of using load monitoring data in data-driven fatigue assessments methodology to evaluate the impact of curtailment scenarios on structural lifetime.…”

Section: Introductionmentioning

confidence: 99%

“…For purpose of this work DEL is the most suitable parameter to assess the impact on lifetime. The DEL is a commonly known fatigue load parameter used in design of foundations, other structural components and subject of ongoing research for farm wide predictions as disclosed in [5] [10]. The present work particularly investigates the role of curtailment regimes with different frequency of stop and start cycles on the consumed lifetime.…”

Section: Introductionmentioning

confidence: 99%

Effect of curtailment scenarios on the loads and lifetime of offshore wind turbine generator support structures

Robbelein

Daems

Verstraeten

et al. 2023

J. Phys.: Conf. Ser.

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Curtailment is a known phenomenon for wind turbine operators of both onshore and offshore wind turbine generators (WTG). Curtailment refers to the situation in which the power output of all WTG’s within a windfarm is forced below the expected power output at the occurring environmental conditions. A direct consequence of curtailment is the loss of power production. In the present contribution further consequences of curtailment of an offshore wind farm (OWF) are studied from the perspective of the support structure, in specific the foundation. In relation to curtailment a couple of potentially critical operational conditions impacting the fatigue consumption of the support structure can be identified. Besides the standstill during operational windspeed conditions, in specific damaging for the +7MW generation WTG’s, curtailment introduces repeated transitions between operational conditions. Since transitions between operational conditions of a WTG are known to be a cause of high fatigue loads in the structural components of the WTG, their increased occurrence due to curtailment might also have an impact on the fatigue consumption of the support structure. With the growing interest of the industry to quantify and potentially optimize the structural lifetime consumption in view of potential lifetime extension of OWF assets, any potential fatigue damaging operational condition is to be investigated. The present work focusses on the investigation of the impact these transitional load cycles may have on the structural lifetime of the WTG foundation. To assess the impact on lifetime, the assessment of the damage equivalent loads (DEL) derived from structural health monitoring (SHM) data are used as a data-driven alternative for model-based load simulations. In the present work such data-driven lifetime assessment studies the impact of curtailment regimes with different frequency of stop and start cycles on the structural lifetime. The study is performed based on 1 year of SHM data collected from two OWF’s. The assessment demonstrates that the impact of additional transitional load cycles on the structural fatigue life consumption is to be considered when defining a long-term curtailment strategy for an OWF.

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Fleetwide Interface Fatigue Load Prediction of an Operational Offshore Wind Farm Using a Single Accelerometer and Scada Data

SANTOS,

NOPPE,

WEIJTJENS

et al. 2023

Proceedings of the 14th International Workshop on Structural Health Monitoring

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The operational life of offshore wind turbines is in part driven by the fatigue life of key structural components, such as the substructure. In recent years, fatigue life management of operational assets has become evermore important, as older farms are closing on their design lifetime and newer farms are designed with tighter margins. To support decisions on fatigue lifetime, it is advantageous to monitor the fatigue progression in these structures through SHM. However, a full instrumentation of every asset in a farm to assess the fatigue life of the substructure is considered economically infeasible. The drive for SHM in wind has been accompanied by the increased availability of intelligent data-driven methodologies which have attempted to provide the same information without the need for additional hardware. One such cost-effective approach, as described in [1], uses the available supervisory control and data acquisition (SCADA) systems, coupled with acceleration measurements to predict the fatigue life of an offshore wind turbine. The use of acceleration measurement data has been proven critical for capturing the complex dynamics of offshore wind turbines. In this contribution, we present the results of said data-driven approach using SCADA and Internet of Things (IoT) accelerometer installed at nacelle-level to monitor the fatigue life for the entirety of a real-world offshore wind farm comprised of 23 turbines, with a specific focus on long-term damage equivalent fatigue loads (DEL) estimation [2]. The availability of acceleration measurements for all locations in particular, is fundamental, as to cover all possible differences in natural frequencies between turbines would be nearly impossible if solely relying on wave and tidal data. To achieve this goal, a neural network architecture is used, enhanced by physics-informed learning focused on long-term estimation, trained and validated on so-called fleet-leader (three turbines instrumented with strain gauges, which provide the ground truth). In this study, we pay special attention to the farm-wide validation, cross-validation and extrapolation of these models as well as the performance for different operational conditions. Finally, this study is undertook for a real-world instrumentation setup, unparalleled in its scale, and can thus be indicative of future trends for SHM in offshore wind: farm-wide instrumentation and monitoring of structural health based on acceleration measurements, enabling a greater trustworthiness on reliability and durability estimation over the serviceable lifetime.

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Towards a Fleetwide Data-Driven Lifetime Assessment Methodology of Offshore Wind Support Structures Based on SCADA and SHM Data

Cited by 4 publications

References 17 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

Effect of curtailment scenarios on the loads and lifetime of offshore wind turbine generator support structures

Fleetwide Interface Fatigue Load Prediction of an Operational Offshore Wind Farm Using a Single Accelerometer and Scada Data

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