Training requirements of a neural network used for fatigue load estimation of offshore wind turbines

Seifert, Janna Kristina; Vera-Tudela, Luis; Kühn, Martin

doi:10.1016/j.egypro.2017.10.356

Cited by 7 publications

(7 citation statements)

References 3 publications

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“…Research by Sharma and Saroha (2015) concluded that a reduction of dimensions possibly leads to a better performance of the mining algorithms while maintaining a good accuracy; therefore, it is important to eliminate potential redundant data and select the variables with the most predictive power for the model. For this, three different feature selection techniques and one dimension reduction technique were applied to the entire dataset and the datasets resulting from filtering the data by operational mode.…”

Section: Dependent Variablesmentioning

confidence: 99%

“…The authors concluded that the minimum training data sample size required is approximately half a month worth of measurements. Seifert et al (2017), acknowledging the complexity and cost of handling extra measurements, assessed the minimum needed size of a training sample to predict fatigue loads using 10 min statistics of SCADA signals and neural networks. In a sense, Seifert et al's (2017) work is an extension or continuation of Vera-Tudela and Kühn's (2014) and Smolka et al's (2013).…”

Section: Introductionmentioning

confidence: 99%

“…In a sense, Seifert et al's (2017) work is an extension or continuation of Vera-Tudela and Kühn's (2014) and Smolka et al's (2013). Seifert et al (2017) tested different sample sizes varying between 1 d (i.e. 144 records) and 4 months (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks

2021

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Abstract. The rapid development of the wind industry in recent decades and the establishment of this technology as a mature and cost-competitive alternative have stressed the need for sophisticated maintenance and monitoring methods. Structural health monitoring has risen as a diagnosis strategy to detect damage or failures in wind turbine structures with the help of measuring sensors. The amount of data recorded by the structural health monitoring system can potentially be used to obtain knowledge about the condition and remaining lifetime of wind turbines. Machine learning techniques provide the opportunity to extract this information, thereby improving the reliability and cost-effectiveness of the wind industry as well. This paper demonstrates the modelling of damage-equivalent loads of the fore–aft bending moments of a wind turbine tower, highlighting the advantage of using the neighbourhood component analysis. This feature selection technique is compared to common dimension reduction/feature selection techniques such as correlation analysis, stepwise regression, or principal component analysis. For this study, recordings of data were gathered during approximately 11 months, preprocessed, and filtered by different operational modes, namely standstill, partial load, and full load. The results indicate that all feature selection techniques were able to maintain high accuracy when trained with artificial neural networks. The neighbourhood component analysis yields the lowest number of features required while maintaining the interpretability with an absolute mean squared error of around 0.07 % for full load. Finally, the applicability of the resulting model for predicting loads in the wind turbine is tested by reducing the amount of data used for training by 50 %. This analysis shows that the predictive model can be used for continuous monitoring of loads in the tower of the wind turbine.

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Section: Dependent Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks

2021

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“…Also, ANN can be used to develop transfer functions that can be used to estimate the ocean current velocities along the length of the marine drilling riser [118] to evaluate the Flapwise Blade roof bending moments [119] , [120] . [121] proposes the monitoring of human fatigue during a marine operation, ANN is used to maintain marine assets such as ship structures, offshore renewable energy platforms, and subsea oil and gas facilities [122] .…”

Section: Fatiguementioning

confidence: 99%

Harbor and coastal structures: A review of mechanical fatigue under random wave loading

Jimenez-Martinez

2021

Heliyon

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Harbor and coastal structures are essential in maritime connections. Additionally, some offshore structures near the coast are important for supplying energy as a material or transforming the natural resources into energy, as in wind turbines. One of the main issues that needs to be overcome in terms of these structures is mechanical fatigue due to the loads of the structure by its function and waves, wind, the current seawater level, and ice. Structural design has to meet high target loads to ensure that structures can endure extreme marine conditions under the assumption of the probability of loads and based on marine conditions, can mask damage where component immersions are not available for inspection. In this work, the wave loads and fatigue damage under random processes are reviewed.

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“…Seifert et al (Seifert et al, 2017) The paper is organized as follows: section 2 outlines the applied methodology in this study, section 3 summarizes the results, 5 and, finally, section 4 presents the conclusions derived from the obtained results.…”

Section: Introductionmentioning

confidence: 99%

Modelling tower fatigue loads of a wind turbine using data mining techniques on SCADA data

Movsessian¹,

Schedat²,

Faber³

2020

Preprint

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Abstract. The rapid development of the wind industry in recent decades and the establishment of this technology as a mature and cost-competitive alternative have stressed the need for sophisticated maintenance and monitoring methods. Structural health monitoring has risen as a diagnosis strategy to detect damage or failures in wind turbine structures with the help of measuring sensors. The amount of data recorded by the structural health monitoring system can potentially be used to obtain knowledge about the condition and remaining lifetime of wind turbines. Machine learning techniques provide the opportunity to extract this information, thereby improving the reliability and cost-effectiveness of the wind industry as well. This paper demonstrates modeling damage equivalent loads of the fore-aft bending moments of a wind turbine tower with the advantage of using the neighborhood component analysis as a feature selection technique in comparison to common dimension reduction/feature selection techniques such as correlation analysis, stepwise regression or principal component analysis. For this study a one-year measuring period of data was gathered, pre-processed, and filtered by different operational modes, namely stand still, full load, and partial load. Finally, a sensitivity analysis was performed in the partial load model to determine the required length of the data collection campaign that guarantees the most precise results. The results indicate that applying neighborhood component analysis yields more conservative models regarding the number of features and equally accurate outcomes than traditional feature selection techniques.

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Training requirements of a neural network used for fatigue load estimation of offshore wind turbines

Cited by 7 publications

References 3 publications

Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks

Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks

Harbor and coastal structures: A review of mechanical fatigue under random wave loading

Modelling tower fatigue loads of a wind turbine using data mining techniques on SCADA data

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