scholarly journals Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 52
Author(s):  
Michael F. Howland ◽  
John O. Dabiri
Keyword(s):  
Wind Farm ◽  
Closed Loop ◽  
Power Production ◽  
Model Parameters ◽  
Closed Loop Control ◽  
Steering Control ◽  
Loop Control ◽  
Large Eddy ◽  
Wake Model ◽  
The Impact

Methods for wind farm power optimization through the use of wake steering often rely on engineering wake models due to the computational complexity associated with resolving wind farm dynamics numerically. Within the transient, turbulent atmospheric boundary layer, closed-loop control is required to dynamically adjust to evolving wind conditions, wherein the optimal wake model parameters are estimated as a function of time in a hybrid physics- and data-driven approach using supervisory control and data acquisition (SCADA) data. Analytic wake models rely on wake velocity deficit superposition methods to generalize the individual wake deficit to collective wind farm flow. In this study, the impact of the wake model superposition methodologies on closed-loop control are tested in large eddy simulations of the conventionally neutral atmospheric boundary layer with full Coriolis effects. A model for the non-vanishing lateral velocity trailing a yaw misaligned turbine, termed secondary steering, is also presented, validated, and tested in the closed-loop control framework. Modified linear and momentum conserving wake superposition methodologies increase the power production in closed-loop wake steering control statistically significantly more than linear superposition. While the secondary steering model increases the power production and reduces the predictive error associated with the wake model, the impact is not statistically significant. Modified linear and momentum conserving superposition using the proposed secondary steering model increase a six turbine array power production, compared to baseline control, in large eddy simulations by 7.5% and 7.7%, respectively, with wake model predictive mean absolute errors of 0.03P1 and 0.04P1, respectively, where P1 is the baseline power production of the leading turbine in the array. Ensemble Kalman filter parameter estimation significantly reduces the wake model predictive error for all wake deficit superposition and secondary steering cases compared to predefined model parameters.

scholarly journals A Steady-State Wind Farm Wake Model Implemented in OpenFAST

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6158
Author(s):  
Antonio Cioffi ◽  
Claudia Muscari ◽  
Paolo Schito ◽  
Alberto Zasso
Keyword(s):  
Wind Farm ◽  
Closed Loop ◽  
Wind Farms ◽  
Closed Loop Control ◽  
Wake Effect ◽  
Loop Control ◽  
Rotor Aerodynamics ◽  
Starting Point ◽  
Real Time Applications ◽  
Wake Model

Wake models play a fundamental role in finding optimized solutions in wind farm control. In fact, they allow assessing how wakes develop and interact with each other with the agility required for real-time applications. In this paper, a Gaussian Wake Model (GWM) is implemented in the OpenFAST framework in a way such that its fidelity is increased with respect to previously implemented models, while enhancing its compatibility with control purposes. The OpenFAST tool is coupled with Floris, NREL’s software based on the GWM, in order to simulate the wake effect on downstream machines (in the case where the downstream rotor is fully covered by the wake, only partially covered by the wake, of the wake is generated by the interaction of more than one turbine), while the rotor aerodynamics is calculated using the BEMT on the actual rotor flow field. We intend this work as a starting point for developing and testing open/closed-loop control logics that will work in real wind farms. To show the suitability of the implementation, the entire model is then compared to Floris.

The Impact of Visual Feedback Type on the Mastery of Visuo-Motor Transformations

2012 ◽  
Vol 220 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Sandra Sülzenbrück
Keyword(s):  
Empirical Research ◽  
Visual Feedback ◽  
Closed Loop ◽  
Motor Planning ◽  
Visual Input ◽  
Closed Loop Control ◽  
Loop Control ◽  
Feedback Type ◽  
Effective Use ◽  
The Impact

For the effective use of modern tools, the inherent visuo-motor transformation needs to be mastered. The successful adjustment to and learning of these transformations crucially depends on practice conditions, particularly on the type of visual feedback during practice. Here, a review about empirical research exploring the influence of continuous and terminal visual feedback during practice on the mastery of visuo-motor transformations is provided. Two studies investigating the impact of the type of visual feedback on either direction-dependent visuo-motor gains or the complex visuo-motor transformation of a virtual two-sided lever are presented in more detail. The findings of these studies indicate that the continuous availability of visual feedback supports performance when closed-loop control is possible, but impairs performance when visual input is no longer available. Different approaches to explain these performance differences due to the type of visual feedback during practice are considered. For example, these differences could reflect a process of re-optimization of motor planning in a novel environment or represent effects of the specificity of practice. Furthermore, differences in the allocation of attention during movements with terminal and continuous visual feedback could account for the observed differences.

scholarly journals The impact of the stimulation frequency on closed-loop control with electrotactile feedback

2015 ◽  
Vol 12 (1) ◽  
Author(s):  
Liliana P Paredes ◽  
Strahinja Dosen ◽  
Frank Rattay ◽  
Bernhard Graimann ◽  
Dario Farina
Keyword(s):  
Closed Loop ◽  
Stimulation Frequency ◽  
Closed Loop Control ◽  
Loop Control ◽  
The Impact

Wind turbine wake position detection and rotor speed-based wake steering validation in a wind tunnel wake simulator

2019 ◽  
Vol 44 (5) ◽  
pp. 483-493
Author(s):  
Ricardo Castillo ◽  
Stephen Bayne ◽  
Suhas Pol ◽  
Carsten Westergaard
Keyword(s):  
Wind Farm ◽  
Closed Loop ◽  
Control Function ◽  
Power Production ◽  
Tip Vortex ◽  
Rotor Speed ◽  
Position Information ◽  
Loop Control ◽  
Position Detection ◽  
Wake Detection

Wind farm control has demonstrated power production improvements using yaw-based wake steering compared to individual turbine optimization. However, slower yaw actuation rates in response to rapid inflow changes lend to impracticality of yaw-based steering, as it causes time-varying downstream rotor–wake overlap, power production fluctuation, and consequent reduction. Therefore, closed-loop wake control is required to mitigate wake deflection uncertainty. To respond to rapid inflow variations, rotor speed actuated is investigated here. Furthermore, wake position information is required as feedback for closed-loop control function. For field-installed turbines, nacelle-based Light detection and ranging (LIDAR) is expected to provide this information. So far, LIDAR-derived wake position has been determined through model-based field reconstruction of scattered LIDAR data. However, this requires sophisticated, economically prohibitive LIDARs. To incorporate inexpensive, two-beam LIDAR for wake detection, a tip vortex-based approach was developed and is also presented here. These contributions can be considered as intermediate steps toward realization of a novel closed-loop wake control.

Brain–Machine Interfaces: Closed-Loop Control in an Adaptive System

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ethan Sorrell ◽  
Michael E. Rule ◽  
Timothy O’Leary
Keyword(s):  
Neural Plasticity ◽  
Closed Loop ◽  
Neural Code ◽  
Annual Review ◽  
Publication Date ◽  
Closed Loop Control ◽  
Brain Machine Interfaces ◽  
Ongoing Research ◽  
Loop Control ◽  
The Impact

Brain–machine interfaces (BMIs) promise to restore movement and communication in people with paralysis and ultimately allow the human brain to interact seamlessly with external devices, paving the way for a new wave of medical and consumer technology. However, neural activity can adapt and change over time, presenting a substantial challenge for reliable BMI implementation. Large-scale recordings in animal studies now allow us to study how behavioral information is distributed in multiple brain areas, and state-of-the-art interfaces now incorporate models of the brain as a feedback controller. Ongoing research aims to understand the impact of neural plasticity on BMIs and find ways to leverage learning while accommodating unexpected changes in the neural code. We review the current state of experimental and clinical BMI research, focusing on what we know about the neural code, methods for optimizing decoders for closed-loop control, and emerging strategies for addressing neural plasticity. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Static and Dynamic Characterization and Control of a High-Performance Electro-Hydraulic Actuator

Actuators ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 46
Author(s):  
Govind N. Sahu ◽  
Suyash Singh ◽  
Aditya Singh ◽  
Mohit Law
Keyword(s):  
Machine Tool ◽  
High Performance ◽  
Closed Loop ◽  
Dynamic Models ◽  
Static Model ◽  
Dynamical Model ◽  
Model Parameters ◽  
Closed Loop Control ◽  
Hydraulic Actuator ◽  
Loop Control

This paper characterizes the static, dynamic, and controlled behavior of a high-performance electro-hydraulic actuator to assess its suitability for use in evaluating machine tool behavior. The actuator consists of a double-acting piston and cylinder arrangement controlled by a servo valve and a separate rear chamber controlled by a separate valve, designed to work in conjunction to generate static forces of up to 7000 N that can be superposed with dynamic forces of up to ±1500 N. This superposition of periodic forces with a non-zero mean makes the actuator capable of applying realistic loading conditions like those experienced by machines during cutting processes. To characterize the performance of this actuator, linearized static and dynamic models are described. Since experiments with the actuator exhibit nonlinear characteristics, the linearized static model is expanded to include the influence of nonlinearities due to flow, leakages, saturations, and due to friction and hysteresis. Since all major nonlinearities are accounted for in the expanded static model, the dynamical model remains linear. Unknown static and dynamical model parameters are calibrated from experiments, and the updated models are observed to capture experimentally observed behavior very well. Validated models are used to tune the proportional and integral gains for the closed-loop control strategy, and the model-based tuning in turn guides appropriate closed-loop control of the actuator to increase its bandwidth to 200 Hz. The statically and dynamically characterized actuator can aid machine tool structural testing. Moreover, the validated models can instruct the design and development of other higher-performance electro-hydraulic actuators, guide the conversion of the actuator into a damper, and also test other advanced control strategies to further improve actuator performance.

Control Strategy of Vehicle Suspension Damping System Based on MATLAB

2012 ◽  
Vol 253-255 ◽  
pp. 2117-2120
Author(s):  
Yu Zhuo Men ◽  
Hai Bo Yu ◽  
Xian Sheng Li ◽  
Yue Wei Li
Keyword(s):  
Working Conditions ◽  
Control Strategy ◽  
Closed Loop ◽  
Vehicle Suspension ◽  
Closed Loop Control ◽  
Lateral Deviation ◽  
Lane Changing ◽  
Loop Control ◽  
Yaw Stability ◽  
The Impact

In order to study the impact of the suspension damping system on the vehicle riding stability, the PID controlling means for suspension damped neural network is presented, implementing a closed-loop control of yaw stability for vehicles. For the two typical working conditions of single lane changing and step steering, the MATLAB software is used for simulation. The result shows that controlling over the vehicle’s lateral deviation movement through suspension damper, it can reduce significantly the load transfers of both the left wheels and right wheels, so that to effectively restrain a vehicle’s over-steering.

scholarly journals Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

2020 ◽  
Author(s):  
Michael F. Howland ◽  
Aditya S. Ghate ◽  
Sanjiva K. Lele ◽  
John O. Dabiri
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
State Estimation ◽  
Wind Farm ◽  
Closed Loop ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Power Production ◽  
Power Reduction ◽  
Wake Model

Abstract. Strategies for wake loss mitigation through the use of dynamic closed-loop wake steering are investigated using large eddy simulations of conventionally neutral atmospheric boundary layer conditions, where the neutral boundary layer is capped by an inversion and a stable free atmosphere. The closed-loop controller synthesized in this study consists of a physics-based lifting line wake model combined with a data-driven Ensemble Kalman filter state estimation technique to calibrate the wake model as a function of time in a generalized transient atmospheric flow environment. Computationally efficient gradient ascent yaw misalignment selection along with efficient state estimation enables the dynamic yaw calculation for real-time wind farm control. The wake steering controller is tested in a six turbine array embedded in a quasi-stationary conventionally neutral flow with geostrophic forcing and Coriolis effects included. The controller increases power production compared to baseline, greedy, yaw-aligned control although the magnitude of success of the controller depends on the state estimation architecture and the wind farm layout. The influence of the model for the coefficient of power Cp as a function of the yaw misalignment is characterized. Errors in estimation of the power reduction as a function of yaw misalignment are shown to result in yaw steering configurations that under-perform the baseline yaw aligned configuration. Overestimating the power reduction due to yaw misalignment leads to increased power over greedy operation while underestimating the power reduction leads to decreased power, and therefore, in an application where the influence of yaw misalignment on Cp is unknown, a conservative estimate should be taken. Sensitivity analyses on the controller architecture, coefficient of power model, wind farm layout, and atmospheric boundary layer state are performed to assess benefits and trade-offs in the design of a wake steering controller for utility-scale application. The physics-based wake model with data assimilation predicts the power production in yaw misalignment with a mean absolute error over the turbines in the farm of 0.02 P1, with P1 as the power of the leading turbine at the farm, whereas a physics-based wake model with wake spreading based on an empirical turbulence intensity relationship leads to a mean absolute error of 0.11 P1.

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