Diagnosing wind turbine faults using machine learning techniques applied to operational data

Leahy, Kevin; Hu, Rong; Konstantakopoulos, Ioannis C.; Spanos, Costas J.; Agogino, Alice M.

doi:10.1109/icphm.2016.7542860

Cited by 96 publications

(62 citation statements)

References 20 publications

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“…In the first example, we preprocess the data sets with baseline normalization and use a KNN model or a feed-forward neural network. More precisely, after the baseline normalization of the data sets, we parameterize/train two supervised learning models: k -nearest neighbors (KNN) with K = 50 neighbors and a feed-forward neural network with three hidden layers with (12,6,6) nodes respectively and rectified linear unit (ReLU) activation functions. We use the cross-entropy as cost function.…”

Section: Resultsmentioning

confidence: 99%

“…The data sets contain time series over periods between 18 and 28 months, and with between five and fifteen individual turbines in each farm. Pairs of farms (1,2), (3,4) and (5,6) have similar classes but also different power curves ( Figure 2). Note that the data sets include neither warning nor status data, although this information might have been used during the manual labeling process.…”

Section: Data Setsmentioning

confidence: 99%

“…Machine learning has previously been applied to wind turbine SCADA data: (i) for example, time-series regression setting for power prediction [2,12,14], and (ii) for fault classification [6]. In this paper, we are interested in multi-class classification of the operational status of power generation for post-construction operational analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we are interested in multi-class classification of the operational status of power generation for post-construction operational analysis. In a previous article, a classifier was applied to wind turbine fault analysis [6] using support vector machines. In that work, multiple binary classifiers were trained on separate test sets for each class.…”

Section: Introductionmentioning

confidence: 99%

“…Another challenge which impedes the industrial application of trained models among different data sets, is the variation between wind turbines: turbine manufacturer, model, geographic location, etc. [3,6,13]. This results in different nacelle anemometer power curves (various cut-in and cut-out wind speeds, rated powers, etc., see Figure 2).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Transferability of Operational Status Classification Models Among Different Wind Turbine Types

Trstanova

Martinsson

Matthews

et al. 2019

J. Phys.: Conf. Ser.

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A detailed understanding of wind turbine performance status classification can improve operations and maintenance in the wind energy industry. Due to different engineering properties of wind turbines, the standard supervised learning models used for classification do not generalize across data sets obtained from different wind sites. We propose two methods to deal with the transferability of the trained models: first, data normalization in the form of power curve alignment, and second, a robust method based on convolutional neural networks and feature-space extension. We demonstrate the success of our methods on real-world data sets with industrial applications.

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Section: Resultsmentioning

confidence: 99%

Section: Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Transferability of Operational Status Classification Models Among Different Wind Turbine Types

Trstanova

Martinsson

Matthews

et al. 2019

J. Phys.: Conf. Ser.

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Lowering post‐construction yield assessment uncertainty through better wind plant power curves

et al. 2021

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Many operational analyses of wind power plants require a statistical relationship, which can be called the wind plant power curve, to be developed between wind plant energy production and concurrent atmospheric variables. Currently, a univariate linear regression at monthly resolution is the industry standard for post-construction yield assessments. Here, we evaluate the benefits in augmenting this conventional approach by testing alternative regressions performed with multiple inputs, at a finer time resolution, and using nonlinear machine-learning algorithms. We utilize the National Renewable Energy Laboratory's open-source software package OpenOA to assess wind plant power curves for 10 wind plants. When a univariate generalized additive model at daily or hourly resolution is used, regression uncertainty is reduced, in absolute terms, by up to 1.0% and 1.2% (corresponding to a À59% and À80% relative change), respectively, compared to a univariate linear regression at monthly resolution; also, a more accurate assessment of the mean long-term wind plant production is achieved. Additional input variables also reduce the regression uncertainty: when temperature is added as an input to the conventional monthly linear regression, the operational analysis uncertainty connected to regression is reduced, in absolute terms, by up to 0.5% (À43% relative change) for wind power plants with strong seasonal variability. Adding input variables to the machine-learning model at daily resolution can further reduce regression uncertainty, with up to a À10% relative change. Based on these results, we conclude that a multivariate nonlinear regression at daily or hourly resolution should be recommended for assessing wind plant power curves.

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Faults Detection and Diagnosis of Concentrated Photovoltaic Systems

Kellil

Aissat

Mellit

2020

Artificial Intelligence and Renewables Towards an Energy Transition

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Diagnosing wind turbine faults using machine learning techniques applied to operational data

Cited by 96 publications

References 20 publications

Transferability of Operational Status Classification Models Among Different Wind Turbine Types

Transferability of Operational Status Classification Models Among Different Wind Turbine Types

Lowering post‐construction yield assessment uncertainty through better wind plant power curves

Faults Detection and Diagnosis of Concentrated Photovoltaic Systems

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