Experimental Characterization and Predictive Modeling of a Residential-Scale Wind Turbine

Chipka, Jordan; Lisicki, Andrew R.; Nguyen, Chuong T.; Taylor, Stuart G; Park, Gyuhae; Ammerman, C. N.; Farrar, Charles R.

doi:10.1007/978-1-4614-6555-3_56

Cited by 2 publications

(2 citation statements)

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“…Finally, the mechanical model, while containing approximations and assumptions, was a sufficiently accurate representation of the experimental turbine for the purpose of this study. A rigorous approach for creating the proper model of the Whisper 500 in FAST has been reported by Chipka et al 25 In order for the data set to be a realistic representation of the real tower site, wind statistics of the LANL site were retrieved from an online meteorological database. 26 These statistics were input to TurbSim to generate full-field stochastic wind speed time series.…”

Section: Description Of Wind Turbine Modelmentioning

confidence: 99%

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Combination of damage feature decisions with adaptive boosting for improving the detection performance of a structural health monitoring framework: Validation on an operating wind turbine

Tsiapoki

Bahrami

Häckell

et al. 2020

Structural Health Monitoring

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This article proposes the deployment of adaptive boosting (AdaBoost) for combining damage feature decisions and improving the detection accuracy of structural health monitoring algorithms. In structural health monitoring applications, damage-sensitive features are combined with classifiers to define decision boundaries and provide information about the structural state. Boosting algorithms combine multiple classifiers aiming at the improvement of their performance. In this study, AdaBoost is deployed on the realizations of a modular structural health monitoring framework, which consists of three tiers: data normalization based on environmental and operational conditions; extraction of damage features, also referred to as condition parameters; and hypothesis testing. Each condition parameter–hypothesis testing pair composes a classifier which is used in AdaBoost as a weak classifier. The integration of AdaBoost with the structural health monitoring framework is validated using experimental data of a 3-kW wind turbine located at the Los Alamos National Laboratory and data generated from a mechanical model of the same structure. The AdaBoost classifier is evaluated with respect to the error rate as well as the true positive and false positive rates, which are typically used in receiver operating characteristic curves. The AdaBoost classifier outperforms the framework classifiers in many cases, improving drastically the detection performance. However, it is shown that the boosting performance depends on the relative location of the condition parameter values on the condition parameter space. The overlaps between the condition parameter values to be combined are quantified using the Bhattacharyya coefficient, which provides a metric for assessing the boosting potential. Finally, omitting condition parameter values corresponding to specific environmental and operational conditions from the boosting process is proposed for obtaining optimum boosting results.

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Section: Description Of Wind Turbine Modelmentioning

confidence: 99%

Section: Description Of Wind Turbine Modelmentioning

confidence: 99%