Axial induction controller field test at Sedini wind farm

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.

Probabilistic surrogates for flow control using combined control strategies

“…If a level of down-regulation is introduced, the reduced thrust might render the yaw-control approach less efficient. However, it should be noted that the downregulation strategy applied in this study is rather simplistic (via constant blade pitch actuator) and can indeed be performed much more intelligently (e.g., both rotational speed and blade pitch as actuators) to maximise power as seen in recent studies [25,26]. The resulting behaviour of the load channels in the power optimisation with and without load constraint is presented in Figure 6, where the constraints are indicated by the light brown…”

Validating the next generation of turbine interaction models

“…A key motivation for development of the sEVM is to support the dynamic simulations required for design and evaluation of wind farm control strategies where active yaw control is used for wake steering to reduce impacts on downstream turbines, aiming to boost wind farm power outputs and control turbine loading [26], [27]. Within the context of this study, we use the sEVM to model steady state conditions, i.e., 10-minute average flow conditions, to explore how the model performs in the context of the EPA.…”

Review of wake management techniques for wind turbines

“…The majority of studies are low‐fidelity simulations and analytical studies that use one of a few popular wake models to approximate the dynamics of wake–turbine and wake–wake interactions 32,34,36–38,41,42,45,49,53,54,59,61,63,65,66,68,70,74,75,77,82,85,89,91,93,94 . Some have used high‐fidelity computational fluid dynamics (CFD) 3,9,26,37,60,62,67,76,81,86,90 or specifically large eddy simulations (LES), 50,55 as well as scaled wind tunnel experiments, 33,39,40,47,48,52,56,69,79,91 and field tests 35,43,44,62,71 . The potential of AIC may have been inflated by the high number of low‐fidelity simulations, which likely report high power gains because they lack sufficient detail to capture critical wake dynamics.…”