Abstract
Abstract. This paper describes the design and testing of an axial induction controller implemented on a row of nine turbines on the Sedini wind farm in Sardinia, Italy. This work was performed as part of the EU Horizon 2020 research project CL-Windcon. An engineering wake model, selected for its good fit to historical SCADA data from the site, was used in the LongSim code to optimise turbine power reduction setpoints for a large matrix of steady-state wind conditions. The setpoints were incorporated into a dynamic control algorithm capable of running on-site using available wind condition estimates from the turbines. The complete algorithm was tested in dynamic time-domain simulations using LongSim, using a time-varying wind field generated from historical met mast data from the site. The control algorithm was implemented on-site, with the wind farm controller toggled on and off at 35 min intervals to allow the performance with and without the controller to be compared in comparable wind conditions. Data were collected between July 2019 and early February 2020. The results have been analysed and indicate a positive increase in energy production resulting from the induction control, in line with LongSim model predictions, although a larger volume of valid data would be necessary to provide statistically robust conclusions. The measurements also provide a validation of the LongSim model, proving its value for both steady-state setpoint optimisation and time-domain simulation of wind farm performance.
Highlights
Wake interactions are well known to reduce wind farm power output and increase turbine loads
An alternative approach is used, in which the lookup table (LUT) calculated for precise wind conditions is smoothed out subsequently, with each value replaced by a weighted average of nearby values, the weightings being determined by those assumed probability distributions
Historical data from the site were first used to confirm a choice of wake model, and the optimiser of the LongSim model was used to generate turbine setpoint lookup tables as a function of wind speed, direction and turbulence intensity which would maximise the power output from the row
Summary
Wake interactions are well known to reduce wind farm power output and increase turbine loads. The control objective is to increase overall wind farm power production while maintaining or reducing turbine fatigue loads, by manipulating the individual turbine controllers to minimise wake interaction effects, using either axial induction control or wake steering. Axial induction control has been investigated using large eddy simulation modelling, often without showing any positive gains in power production – see for example Gebraad (2014) It has since been tested in a boundary layer wind tunnel by Campagnolo et al (2016a, b) as well as in an operational wind farm by van der Hoek et al (2019).
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