Wind Turbine Power Curve Monitoring Based on Environmental and Operational Data

Megawatt-scale wind turbine technology is nowadays mature and, therefore, several technical improvements in order to optimize the efficiency of wind power conversion have been recently spreading in the industry. Due to the nonstationary conditions to which wind turbines are subjected because of the stochastic nature of the source, the quantification of the impact of wind turbine power curve upgrades is a complex task and in general, it has been observed that the efficiency of the upgrades can vary considerably depending on the wind flow conditions at the microscale level. In this work, a test case of wind turbine control system improvement was studied numerically and through operational data. The wind turbine is multi-megawatt; it is part of a wind farm sited in a complex terrain in Italy, featuring 17 wind turbines. The analyzed control upgrade is an optimization of the revolutions per minute (rpm) management. The impact of this upgrade was quantified through a method based on operational data: It consists of the study, before and after the upgrade, of the residuals between the measured power output of the wind turbine of interest and an appropriate model of the power output itself. The input variables for the model were selected to be some operational parameters of the nearby wind turbines: They were selected from the data set at disposal with a stepwise regression algorithm. This work also includes a numerical characterization of the problem, by means of aeroelastic simulations performed with the FAST software: By mimicking the pre- and post-upgrade generator rpm–generator torque curve, it is subsequently possible to estimate how the wind turbine power curve changes. The main result of this work is that the two estimates of production improvement have the same order of magnitude (1.0% of the production below rated power). In general, this study sheds light on the perspective of employing not only operational data, but also a sort of digital replica of the wind turbine of interest, in order to reliably quantify the impact of control system upgrades.

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Research on wind turbine power performance based on the operation Data

E3S Web of Conferences ◽

10.1051/e3sconf/201913602002 ◽

2019 ◽

Vol 136 ◽

pp. 02002

Author(s):

Guanglei Li ◽

Dehua Wang ◽

Xiaoliang Liu ◽

Peng Zhao

Keyword(s):

Wind Turbine ◽

Power Curve ◽

Power Performance ◽

Operation Performance ◽

Standard Specification ◽

Power Characteristics ◽

Improvement Measures ◽

And Performance ◽

Operational Data ◽

Measured Wind Speed

The power characteristics are important for evaluating the operating state of the wind turbine. In order to accurately evaluate the performance of the actual 2.0MW wind turbine, this paper firstly collects the measured wind speed, power and other operational data reflecting the performance of the wind turbine, and analyzes the data according to the standard specification. On the basis of data analysis, this paper obtains the actual power curve of wind turbine, and then compares it with the theoretical power curve. Finally, this paper carries out comparative analysis and performance evaluation of wind turbine operation performance, and propose improvement measures on this basis.

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Quantile based probabilistic wind turbine power curve model

Applied Energy ◽

10.1016/j.apenergy.2021.116913 ◽

2021 ◽

Vol 296 ◽

pp. 116913

Author(s):

Keyi Xu ◽

Jie Yan ◽

Hao Zhang ◽

Haoran Zhang ◽

Shuang Han ◽

...

Keyword(s):

Wind Turbine ◽

Power Curve ◽

Curve Model

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Multi-objective grey wolf optimization of solar chimneys based on an improved model incorporating a wind turbine power curve

Energy Conversion and Management ◽

10.1016/j.enconman.2021.114231 ◽

2021 ◽

Vol 239 ◽

pp. 114231

Author(s):

Ali Habibollahzade ◽

Iman Fakhari ◽

Saeed Mohsenian ◽

Hossein Aberoumand ◽

Robert A. Taylor

Keyword(s):

Wind Turbine ◽

Power Curve ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Multi Objective ◽

Improved Model

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Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Energies ◽

10.3390/en14041105 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1105 ◽

Cited By ~ 2

Author(s):

Davide Astolfi ◽

Francesco Castellani ◽

Andrea Lombardi ◽

Ludovico Terzi

Keyword(s):

Standard Deviation ◽

Wind Turbine ◽

Wind Turbines ◽

Curve Analysis ◽

Gaussian Kernel ◽

Support Vector ◽

Features Selection ◽

Power Curve ◽

Working Parameters ◽

Extracted Power

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

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