Numerical and Experimental Methods for the Assessment of Wind Turbine Control Upgrades

Abstract

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.

Wind turbine generator slip ring damage detection through temperature data analysis

“…The target of the model has therefore been selected as the generator slip ring temperature (relative to the ambient temperature) and the possible input variables for the model are all the other measurements listed in Section 1. As discussed for example in [30][31][32], a linear model is commonly adequate for this kind of tasks. A subtle point regards the fact that there likely is a remarkable collinearity between the possible input variables of the model (for example, wind speed and power production) and this can affect the quality of the model.…”

Special Issue on “Wind Energy Conversion Systems”

“…The impacts of these design variables are explored, which could provide useful information for wind turbine design and operation. In the paper "Numerical and Experimental Methods for the Assessment of Wind Turbine Control Upgrades" [7], the impacts of wind turbine control system upgrade have been studied numerically with operational data; it may be used as a tool to evaluate the cost effectiveness of wind turbine control system improvement. The paper "Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function" has discussed the representation of a wind turbine power curve by means of the cumulative distribution function (CDF) of a Weibull distribution [8].…”

Wind Turbine Yaw Control Optimization and Its Impact on Performance

“…This side-by-side method has been conceptually generalized in [41], where a combined aerodynamic and control upgrade is studied and the power of the wind turbine of interest is modeled through linear regression: the input variables for the model result to be operation parameters (power, blade pitches, rotor revolutions per minute and so on) of the nearby wind turbines and are selected among all the possible covariates at disposal through stepwise regression algorithm. A similar approach has been adopted also in [42], while in [43,44] non-linear regression methods (like Artificial Neural Networks) are employed for assessment studies of wind turbine power curve upgrades.…”