Wind Turbine Multivariate Power Modeling Techniques for Control and Monitoring Purposes

Abstract

Wind turbine performance monitoring is a complex task because the power has a multivariate dependence on ambient conditions and working parameters. Furthermore, wind turbine nacelle anemometers are placed behind the rotor span and the control system estimates the upwind flow through a nacelle transfer function: this introduces a data quality issue. This study is devoted to the analysis of data-driven techniques for wind turbine performance control and monitoring: operation data of six 850 kW wind turbines sited in Italy have been employed. The objective of this study is an assessment of several easily implementable techniques and input variables selections for data-driven models whose target is the power of a wind turbine. Three model types are selected: one is linear (Principal Component Regression) and two are non-linear (Support Vector Regression with Gaussian Kernel and Feedforward Artificial Neural Network). The models' validation provides meaningful indications: the linear model in general has lower performance because it can not reproduce properly the non-linear pitch behavior when approaching rated power. Therefore, it is concluded that a non-linear model should be employed and the achieved mean absolute error is of the order of 1.3% of the rated power. Furthermore, the errors are kept at the order of 2% of the rated power for the models whose input is the rotor speed instead that wind speed: this observation supports that, in case it is needed because of nacelle anemometer biases, the power monitoring can be acceptably implemented using the rotor speed.

Estimation of the Performance Aging of the Vestas V52 Wind Turbine through Comparative Test Case Analysis

“…For these reasons, in order to compare reliably the performance of the same model of wind turbine placed in different sites, it is more appropriate to compare operation curves that are not based on the nacelle wind speed measurements: in this study, the same curves as in [25] are selected, which are the generator speed-power curve and the blade pitchpower curve. In studies like [34][35][36], it was observed that operation variables as the rotor speed, generator speed, and blade pitch are important for a reliable multivariate analysis of the power of a wind turbine [37,38]. In this study, following [38], a step forward with respect to the literature is made because the selected curves involve couples of operation variables and do not involve the nacelle wind speed.…”

“…In studies like [34][35][36], it was observed that operation variables as the rotor speed, generator speed, and blade pitch are important for a reliable multivariate analysis of the power of a wind turbine [37,38]. In this study, following [38], a step forward with respect to the literature is made because the selected curves involve couples of operation variables and do not involve the nacelle wind speed. In [25], it has been observed that the above curves should be interpreted in light of the control of the wind turbines: for moderate wind speed (approximately 5 ≤ v ≤ 9 m/s), wind turbines operate with a variable rotational speed and fixed pitch; for higher wind speeds (approximately 9 ≤ v ≤ 13 m/s), the rotational speed is rated and the blade pitch varies.…”

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

“…From Table 3 it arises that the average error metrics can be considered particularly robust because their standard deviation is very low. Furthermore, as regards the power P it is possible to compare the literature: the order of the error metrics is similar to the results collected in [36] for the same test case. Therefore it can be stated that the dimension reduction through the PCA does not affect remarkably the quality of the regression and helps highlighting the dependence of the target on the orthogonalized input variables matrix.…”

Wind Turbine Operation Curves Modelling Techniques

“…This inspires as further direction to adopt a similar approach also for multivariate wind turbine power curve modelling: actually, there is a wide literature about the improvement of the model structure, while the discussion about the use of further covariates is at its early stages. In [24,25,[27][28][29], multivariate models for the power curves have been proposed, which include further environmental and operation variables with respect to solely the average nacelle wind speed, but at present there are no studies dealing with the inclusion in the models of minimum, maximum and standard deviation of the main variables. Finally, it would be extremely interesting to analyze the operation curves selected in this study by using time-resolved wind turbine data [47] having much lower sampling time (order of seconds).…”

“…The simplest data-driven model employed for wind turbine performance monitoring is the power curve [20][21][22][23], which is the relation between the average wind speed and the extracted power. On the grounds of the above discussion, a recent trend in wind energy literature regards multivariate approaches to the power curve: in [24][25][26], the point of view is including additional environmental variables (either measured ambient temperature, humidity, and wind direction as in [24] or estimated by a Numerical Weather Prediction model as in [25]); in [27][28][29][30], the approach consists in including the most important operation variables (as the blade pitch and the rotor speed) in the multivariate modelling of the power curve.…”