Modeling wind-turbine power curve: A data partitioning and mining approach

Ouyang, Tinghui; Kusiak, Andrew; He, Yusen

doi:10.1016/j.renene.2016.10.032

Cited by 167 publications

(99 citation statements)

References 18 publications

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“…Recently, machine learning and neural networks that derive multidimensional power curve models have grown in popularity 95 (Bessa et al, 2012;Jeon and Taylor, 2012;Lee et al, 2015;Optis and Perr-Sauer, 2019;Ouyang et al, 2017;Pandit and Infield, 2018;Pelletier et al, 2016).…”

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

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“…The wind turbine power at each wind speed is influenced by a great deal of factors, both stochastic and predetermined [2,3,7,11,13]. The curvilinearity of the power curve flanks is implicitly formed in a similar way.…”

Section: An Exponential Model Of the Wind Turbine Power Curvementioning

confidence: 99%

“…If a sizeable number of training and testing data is available, then non-parametric techniques based on data mining techniques and neural networks perform well. The performance of the wind turbine power curve modeled using four and five parameter logistic expressions is reported to outperform the linearized segmented model and the models based on neural network, fuzzy logic and data mining algorithms [9,11,13,14]. Thus, it is expected that such a outperformance shall exist for poorer initial and antecedent data.…”

Section: Introductionmentioning

confidence: 99%

“…According to [11,13], the models based on the concept of power available in the wind, like the probabilistic and polynomial models, do not give accurate results. This is because of the fact that the fraction of wind power that is converted to electrical power depends on several other parameters like rotational speed of the turbine, turbine blade parameters, and the efficiencies of the mechanical transmission system and generator efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Wind Turbine Power Curve Exponential Model with Differentiable Cut-in and Cut-out Parts

Romanuke

2018

Nauk. Visti KPI

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WIND TURBINE POWER CURVE EXPONENTIAL MODEL WITH DIFFERENTIABLE CUT-IN AND CUT-OUT PARTSBackground. The main characteristic of a wind turbine is its power curve. Getting measurement data off powerful wind turbines is a way harder than measuring characteristics of wind turbines for individual/home use. A yet significant gap is that all wind turbines have a few similarities in their power curves but they do not have a formalized description, which could help in selecting better turbines fitting specific areas (without precise measurements in a vicinity of cut-in and cut-out speeds).Objective. As there is a straight lack of mathematical description of wind turbine power curves, the goal is to obtain a model of such curves. Methods. A power curve is of seven parts. Factual power curves remotely remind trapezia with curvilinear flanks. Because of inertia, the curvilinearity is severer for those wind turbines whose output power is greater. As blades of industrial wind turbines are too massive, their inertia makes those lag effects, that could be modeled by using natural smoothness of power curves. For describing that smoothness along with the curvilinearity, we use two increasing and two decreasing exponential functions for the flanks. Results. A wind turbine power output function consists of two zero parts, one rated-out part, and the suggested four exponential parts. The cut-in parts are described with two increasing exponential functions whose exponential growth factors are equal. The cut-out parts are described with two decreasing exponential functions whose exponential decrease factors are equal also. Such equal factors ensure strong differentiability of the power curve within those parts.Conclusions. The exponential model is for a general description of the wind turbine power curve. Having differentiable cut-in and cut-out parts, it suggests the "natural smoothing" that happens in reality due to highly-inertial wind turbine blades. The model is not necessarily to be used to fit some experimental data, but rather for patterning power curves.

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“…One might therefore possibly invoke other kinds of precision modeling of wind turbines power curve. It actually is a very fertile field in the scientific literature [14][15][16][17]: as regards these models, the main drawback for their application to the study of wind turbine upgrades is that they are too complex and not enough flexible to be applied to the range of different criticality that the study of wind turbine upgrades poses.The above shortcomings are both circumvented in the present study: this work actually is a collaboration between academia and industry. The industry is Renvico srl 1 , owning and managing 335 MW of full-scale wind turbines in Italy and France.…”

mentioning

confidence: 99%

Wind Turbine Power Curve Upgrades

Astolfi¹,

Castellani²,

Terzi³

2018

Preprint

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Full-scale wind turbine technology has been widely developing in the recent years and condition monitoring techniques assist at the scope of making 100% technical availability a realistic perspective. In this context, several retrofitting techniques are being used for further improving the efficiency of wind kinetic energy conversion. This kind of interventions is costly and, furthermore, the estimation of the energy enhancement is commonly provided under the hypothesis of ideal conditions, as for example absence of wakes between nearby turbines. A precise quantification of the energy gained by retrofitting is therefore precious in real conditions, that can be very different from ideal ones. In this work, three kinds of retrofitting are studied through the operational data of test case wind farms: improved start-up through pitch angle adjustment near the cut-in, aerodynamic blade retrofitting by means of vortex generators and passive flow control devices, extension of the power curve by raising cut-out and high wind speed cut-in. SCADA data are employed and reliable methods are formulated for estimating the energy improvement from each of the above retrofitting. Further, an insight is provided about wind turbine functioning under very stressing regimes, as for example high wind speeds.retrofitting; further, for some reason as in one of the test cases of the present work, it might happen that the approach can't be employed at all. One might therefore possibly invoke other kinds of precision modeling of wind turbines power curve. It actually is a very fertile field in the scientific literature [14][15][16][17]: as regards these models, the main drawback for their application to the study of wind turbine upgrades is that they are too complex and not enough flexible to be applied to the range of different criticality that the study of wind turbine upgrades poses.The above shortcomings are both circumvented in the present study: this work actually is a collaboration between academia and industry. The industry is Renvico srl 1 , owning and managing 335 MW of full-scale wind turbines in Italy and France. Some wind turbines owned by Renvico have been retrofitted in several ways: this has been a pilot test and the decision of extending the retrofitting to the other wind turbines in the corresponding wind farms has been based on the assessment, coming from this study, of the real profit on the pilot test cases. The SCADA data from the retrofitted and non-retrofitted wind turbines from the wind farms of interest have therefore been shared with the University of Perugia. This academia-industry collaboration, as shall be shown in the next Sections, has produced methods for the study of power curve upgrades, standing at the frontier of the scientific debate and being as well integrable in the everyday industrial management of the wind turbines. This kind of studies on this subject, basing on operational data, has been very recently developing in the scientific literature: in [18], a kernel plus method is adopted for computing ...

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Modeling wind-turbine power curve: A data partitioning and mining approach

Cited by 167 publications

References 18 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 Exponential Model with Differentiable Cut-in and Cut-out Parts

Wind Turbine Power Curve Upgrades

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