35 citations · 160 across the 13 of their papers we have counts for
13 papers
Data-driven predictive control of nonlinear systems using weighted regularization
Fritz A. Engeln, Sebastian Zieglmeier, Marta Zagórowska +1
Data-driven control methods, like Data-enabled Predictive Control (DeePC), are often formulated for linear systems, where the principle of superposition allows global system behavi…
Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control
Rogier Dinkla, Sebastiaan Mulders, Tom Oomen +1
Factors like improved data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control…
Maximizing wind farm power output with the helix approach -- experimental validation and wake analysis using tomographic PIV
Daan van der Hoek, Bert Van den Abbeele, Carlos Simao Ferreira +1
Wind farm control can play a key role in reducing the negative impact of wakes on wind turbine power production. The helix approach is a recent innovation in the field of wind farm…
Optimal Control for Wind Turbine Wake Mixing on Floating Platforms
Maarten J. van den Broek, Daniel van den Berg, Benjamin Sanderse +1
Dynamic induction control is a wind farm flow control strategy that utilises wind turbine thrust variations to accelerate breakdown of the aerodynamic wake and improve downstream t…
Adjoint Optimisation for Wind Farm Flow Control with a Free-Vortex Wake Model
Maarten J. van den Broek, Delphine De Tavernier, Benjamin Sanderse +1
Wind farm flow control aims to improve wind turbine performance by reducing aerodynamic wake interaction between turbines. Dynamic, physics-based models of wind farm flows have bee…
Active power control of wind farms: an instantaneous approach on waked conditions
Jean Gonzalez Silva, Bart Matthijs Doekemeijer, Riccardo Ferrari +1
This paper presents a closed-loop controller for wind farms to provide active power control services using a high-fidelity computational fluid dynamics based wind plant simulator.…
Damping Identification of an Operational Offshore Wind Turbine using Kalman filter-based Subspace Identification
Aemilius A W van Vondelen, Alexandros Iliopoulos, Sachin T Navalkar +2
Operational Modal Analysis (OMA) provides essential insights into the structural dynamics of an Offshore Wind Turbine (OWT). In these dynamics, damping is considered an especially…
The Proportional Integral Notch and Coleman Blade Effective Wind Speed Estimators and Their Similarities
Yichao Liu, Atindriyo K. Pamososuryo, Sebastiaan P. Mulders +2
The estimation of the rotor effective wind speed is used in modern wind turbines to provide advanced power and load control capabilities. However, with the ever increasing rotor si…
The Immersion and Invariance Wind Speed Estimator Revisited and New Results
Yichao Liu, Atindriyo Kusumo Pamososuryo, Riccardo M. G. Ferrari +1
The Immersion and Invariance (I&I) wind speed estimator is a powerful and widely-used technique to estimate the rotor effective wind speed on horizontal axis wind turbines. Anyway,…
Preventing wind turbine tower natural frequency excitation with a quasi-LPV model predictive control scheme
Sebastiaan Paul Mulders, Tobias Gybel Hovgaard, Jacob Deleuran Grunnet +1
With the ever increasing power rates of wind turbines, more advanced control techniques are needed to facilitate tall towers that are low-weight and cost effective, but in effect m…
A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model
Bart M Doekemeijer, Paul A Fleming, Jan-Willem van Wingerden
In wind farms, wake interaction leads to losses in power capture and accelerated structural degradation when compared to freestanding turbines. One method to reduce wake losses is…
Data-driven repetitive control: Wind tunnel experiments under turbulent conditions
Joeri Frederik, Lars Kröger, Gert Gülker +1
A commonly applied method to reduce the cost of wind energy, is alleviating the periodic loads on turbine blades using Individual Pitch Control (IPC). In this paper, a data-driven…
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization
Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden +1
Bayesian optimization through Gaussian process regression is an effective method of optimizing an unknown function for which every measurement is expensive. It approximates the obj…