Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling

Abstract

Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated.

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

“…This result is interesting because it is typical that, vice versa, the near-rated region is the most critical as regards power monitoring. It was noticeable that, despite the simplifications of the proposed methods, the obtained average error metrics were competitive with the state-of-the-art in the literature, as can be argued by the discussion in Section 4.3 and by comparing against Table 1 in [33].…”

“…Despite it have been shown that the wind speed can account for up to the 99% of the variance of the power [32] and therefore further input variables can explain not more than the residue of 1%, this can be decisive in order to obtain data-driven models whose average error metrics are sufficiently low to guarantee a robust monitoring of wind turbine performance. For a recent review about multivariate wind turbine power curves, refer to [33].…”

“…From the discussion in [33], it arises that the literature about wind turbine multivariate power curves is at its early stages, but some evidence has been gradually accumulating:…”

A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging

“…Given above considerations, there are two keys for WTG OP: (1) how to use or obtain unstable environmental conditions and variable operating conditions as input variables for WTG OP; (2) and the multidimensional variable power curve modeling method. For (1), past research demonstrates the benefit of using multivariate (WTG operation conditions source from SCADA and ambient conditions from met mast) in a power performance test [13][14][15][16][17]. Reference [13] used the blade pitch (SCADA), rotational speed (SCADA), temperature (met mast), and nacelle wind speed (SCADA) as the inputs for power curve assessment, and achieved more accurate results.…”

“…To sum, using more suitable SCADA variables and met mast variables to replace the single wind speed factor have become the trend of wind turbine power characteristic evaluation. For (2), the non-linear relationship between input variables and WTG OP needs modeling, commonly used non-parametric techniques [16], including the Gaussian process [18], neural networks [19], support vector machine [20], and polynomial LASSO regression [13]. They have demonstrated the effectiveness of machine learning in power curve modeling.…”