A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Systems

Sohoni, Vaishali; Gupta, Sushma; Nema, R. K.

doi:10.1155/2016/8519785

Cited by 192 publications

(115 citation statements)

References 63 publications

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“…Introducing modern data with data-driven statistical methods to improve power modeling techniques has been the new direction of the wind energy industry. Past research demonstrates the benefit of using remote sensing and supervisory control and data acquisition (SCADA) data in power performance tests (Demurtas et al, 2017;Hofsäß et al, 2018;Mellinghoff, 90 2013;Rettenmeier et al, 2014;Sohoni et al, 2016;Wagner et al, 2013Wagner et al, , 2014. Many experts use the PDM approach (Fig.…”

Section: The Challengementioning

confidence: 99%

The Power Curve Working Group's Assessment of Wind Turbine Power Performance Prediction Methods

Lee

Stuart

Clifton

et al. 2019

Preprint

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Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that 15 reduce error and uncertainty in power prediction when wind shear and turbulence digress from design conditions. Herein, we analyze the data of 55 wind turbine power performance tests from 9 contributing organizations with statistical tests to quantify the skills of the prediction-correction methods. We assess the accuracy and precision of four proposed trial methods against the Baseline method, which uses the conventional definition of power curve with wind speed and air density at hub height.The trial methods reduce power-production prediction errors compared to the Baseline method at high wind speeds, which 20 contribute heavily to power production; however, the trial methods fail to significantly reduce prediction uncertainty in most meteorological conditions. For the meteorological conditions when a wind turbine produces less than the power its reference power curve suggests, using power deviation matrices leads to more accurate power prediction. We also identify that for more than half of the submissions, the data set has a large influence on the effectiveness of a trial method. Overall, this work affirms the value of data-sharing efforts in advancing power-curve modeling and establishes the groundwork for future collaborations. 25

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Section: The Challengementioning

confidence: 99%

The Power Curve Working Group's Assessment of Wind Turbine Power Performance Prediction Methods

Lee

Stuart

Clifton

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…In [13], a three-parameter logistic function (3-PLF) was used by the authors to evaluate the performance of the Gamsesa G90-2.0 MV wind turbine. Many researchers suggested using a four-parameter logistic function (4-PLF) to model WTPC [14,15]. When the variable asymmetry factor is added into the above 4-PLF, a five-parametrer logistic function (5-PLF) can be derived.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique

Pei

2019

Applied Sciences

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A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.

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“…Thirdly, the system status may change in the historical data, such as degradation. Awareness of degradation can help improve turbines' reliability and on-site power curve estimation [5].…”

Section: Introductionmentioning

confidence: 99%

Phase I Analysis of Hidden Operating Status for Wind Turbine

Shi

Chen

2019

2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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Data-driven methods based on Supervisory Control and Data Acquisition (SCADA) becomes a recent trend for wind turbine condition monitoring. However, SCADA data are known to be of low quality due to low sampling frequency and complex turbine working dynamics. In this work, we focus on the phase I analysis of SCADA data to better understand turbines' operating status. As one of the most important characterization, the power curve is used as a benchmark to represent normal performance. A powerful distribution-free control chart is applied after the power generation is adjusted by an accurate power curve model, which explicitly takes into account the known factors that can affect turbines' performance. Informative out-of-control segments have been revealed in real field case studies. This phase I analysis can help improve wind turbine's monitoring, reliability, and maintenance for a smarter wind energy system.

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A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Systems

Cited by 192 publications

References 63 publications

The Power Curve Working Group's Assessment of Wind Turbine Power Performance Prediction Methods

The Power Curve Working Group's Assessment of Wind Turbine Power Performance Prediction Methods

Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique

Phase I Analysis of Hidden Operating Status for Wind Turbine

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