An Innovative Correction Method of Wind Speed for Efficiency Evaluation of Wind Turbines

Carullo, Alessio; Ciocia, Alessandro; Malgaroli, Gabriele; Spertino, Filippo

doi:10.21014/acta_imeko.v10i2.1037

Cited by 17 publications

(13 citation statements)

References 12 publications

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“…How to correct the wind speed has been an issue of great concern. For example, Malgaroli et al propose a nacelle wind speed correction for evaluating wind turbine performance by estimating the wind speed entering the wind rotor (Astolfi et al, 2021b;Carullo et al, 2021). In this paper, the nacelle wind speed compensation is mainly based on the aerodynamic and energy flow characteristics of the wind turbines (Dai et al, 2016b).…”

Section: Wind Speed Data Correctionmentioning

confidence: 99%

Study on Obtaining Real Power Curve of Wind Turbines Using SCADA Data

et al. 2022

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The key problem to be solved in the process of wind turbine (WT) operation and maintenance is to obtain the wind turbine performance accurately. The power curve is an important indicator to evaluate the performance of wind turbines. How to model and obtain the power curve of wind turbines has always been one of the hot topics in research. This paper proposes a novel idea to get the actual power curve of wind turbines. Firstly, the basic data preprocessing algorithm is designed to process the zero value and null value in the original supervisory control and data acquisition (SCADA) data. The moving average filtering (MAF) method is employed to deal with the wind speed, the purpose of which is to consider the comprehensive result of wind on the wind turbine power in a certain period. According to the momentum theory of the ideal wind turbine and combined with the characteristics of the anemometer installation position, the deviation between the measured wind speed and the actual wind speed is approximately corrected. Here, the influence of dynamic changes in air density is also considered. Then, the Gaussian fitting algorithm is used to fit the wind-power curve. The characteristics of the power curve before and after wind speed correction are compared and analyzed. At the same time, the influence of the parameter uncertainty on the reliability of the power curve is considered and investigated. Finally, the characteristics of the power curves of four wind turbines are compared and analyzed. The research results show that among these power curves, WT3 and WT4 are the closest, WT2 is the next, and WT1 has the farthest deviation from the others. The research work provides a valuable basis for on-site performance evaluation, overhaul, and maintenance of wind turbines.

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Section: Wind Speed Data Correctionmentioning

confidence: 99%

Study on Obtaining Real Power Curve of Wind Turbines Using SCADA Data

et al. 2022

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“…To set the hyperparameter λ, k-fold (with k = 10) cross-validation was performed [50]. Equation (7) was employed for simulating the output on the target data set D 2 , and the performance of the model, given the residuals between the model estimated and measurements (Equation ( 8)), was quantified through the most common metrics: MAE (Equation ( 9)), MAPE (Equation ( 10)), and RMSE (Equation ( 11)).…”

Section: Covariatementioning

confidence: 99%

“…The power curve analysis has the great advantage of simplicity, but the drawback is that it does not account for the fact that the power of a wind turbine has a multivariate dependence on the environmental conditions and working parameters [5]. Furthermore, the undisturbed wind flow is not measured directly: it is estimated through a nacelle transfer function based on downwind measurements collected behind the rotor span [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Wind Turbine Power Curve Model Based on Data Clustering and Polynomial LASSO Regression

Astolfi

Pandit

2021

Applied Sciences

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Wind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The k-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization.

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“…For example, in [3], it was reported that in 2020, 28% of wind turbines installed in Europe were older than 15 years of age, with peaks in the order of 50% in Spain, Germany, and Denmark. This matter of fact motivates a recent deeper attention to the analysis and the interpretation of wind turbine performance, which is in general complicated because there are issues related with quantity and quality of nacelle anemometer data [4,5]. It is generally expected that the performance of whatever technical system declines with age [6,7] and the growing amount of aged operating wind turbines poses the issue of wind turbine performance analysis with a major urgency.…”

Section: Introductionmentioning

confidence: 99%

Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis

Astolfi

Pandit

Terzi³

et al. 2022

Energies

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This work is devoted to the formulation of innovative SCADA-based methods for wind turbine performance analysis and interpretation. The work is organized as an academia–industry collaboration: three test cases are analyzed, two with hydraulic pitch control (Vestas V90 and V100) and one with electric pitch control (Senvion MM92). The investigation is based on the method of bins, on a polynomial regression applied to operation curves that have never been analyzed in detail in the literature before, and on correlation and causality analysis. A key point is the analysis of measurement channels related to the blade pitch control and to the rotor: pitch manifold pressure, pitch piston traveled distance and tower vibrations for the hydraulic pitch wind turbines, and blade pitch current for the electric pitch wind turbines. The main result of this study is that cases of noticeable under-performance are observed for the hydraulic pitch wind turbines, which are associated with pitch pressure decrease in time for one case and to suspected rotor unbalance for another case. On the other way round, the behavior of the rotational speed and blade pitch curves is homogeneous and stable for the wind turbines electrically controlled. Summarizing, the evidence collected in this work identifies the hydraulic pitch as a sensible component of the wind turbine that should be monitored cautiously because it is likely associated with performance decline with age.

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An Innovative Correction Method of Wind Speed for Efficiency Evaluation of Wind Turbines

Cited by 17 publications

References 12 publications

Study on Obtaining Real Power Curve of Wind Turbines Using SCADA Data

Study on Obtaining Real Power Curve of Wind Turbines Using SCADA Data

Multivariate Wind Turbine Power Curve Model Based on Data Clustering and Polynomial LASSO Regression

Discussion of Wind Turbine Performance Based on SCADA Data and Multiple Test Case Analysis

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