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