Data Driven Modelling of the Dynamic Wake Between Two Wind Turbines

Abstract

Wind turbines in a wind farm, influence each other through the wind flow. Downwind turbines are in the wake of upwind turbines and the wind speed experienced at downwind turbines is hence a function of the wind speeds at upwind turbines but also the momentum extracted from the wind by the upwind turbine. This paper establishes flow models relating the wind speeds at turbines in a farm. So far, research in this area has been mainly based on first principles static models and the data driven modelling done has not included the loading of the upwind turbine and its impact on the wind speed downwind. This paper is the first where modern commercial mega watt turbines are used for data driven modelling including the upwind turbine loading by changing power reference. Obtaining the necessary data is difficult and data is therefore limited. A simple dynamic extension to the Jensen wake model is tested without much success. The best model turns out to be non linear with upwind turbine loading and wind speed as inputs. Using a transformation of these inputs it is possible to obtain a linear model and use well proven system identification methods. Finally it is shown that including the upwind wind direction to explain the wake improve the prediction performance.

Survey of wind farm control—power and fatigue optimization

“…The dynamics between upwind WT loading and downwind WT local EWS consist of several components, a time constant for building up the upwind wake after a change in upwind WT loading, the transport delay , the meandering and mixing that blurs the wake deficit. Knudsen and Bak developed a simple transfer function model that demonstrates that this dynamic relation between upwind WT loading change, by power reference change, and downwind EWS can be identified from a full‐scale experiment.…”

An experimental investigation on the effect of individual turbine control on wind farm dynamics

“…An improved understanding of the aerodynamic interactions between turbines can aid in the design of enhanced control strategies that coordinate all turbines in a farm. There have been some studies in the field and in wind tunnels looking at these aerodynamic interactions . The coordinated turbine control strategies presented in the literature aim to increase the total wind farm power and, in some cases, reduce the structural loads.…”

“…There have been some studies in the field and in wind tunnels looking at these aerodynamic interactions. [6][7][8] The coordinated turbine control strategies presented in the literature aim to increase the total wind farm power and, in some cases, reduce the structural loads. The essential idea is that reducing the efficiency of the lead turbines, referred to hereafter as derating, results in higher wind speeds for downstream turbines.…”

The effect of timescales on wind farm power variability with nonlinear model predictive control

“…In addition, the applicability of the reported results will broaden as the accuracy of wake models improves. This is an active field of research with researchers working to improve such aspects as time delay modeling, transfer functions for how the input velocity changes across a turbine and the velocity deficit in turbulent wakes . A recent study using a higher‐order wake model has even called into question previous findings that axial induction‐based methods of cooperative control provide a net increase in the net wind farm power over individual turbine control methods .…”