Automatic Retraction and Full Cycle Operation for a Class of Airborne Wind Energy Generators

Airborne wind energy systems aim to harvest the power of winds blowing at altitudes higher than what conventional wind turbines reach. They employ a tethered flying structure, usually a wing, and exploit the aerodynamic lift to produce electrical power. In the case of ground-based systems, where the traction force on the tether is used to drive a generator on the ground, a two phase power cycle is carried out: one phase to produce power, where the tether is reeled out under high traction force, and a second phase where the tether is recoiled under minimal load. The problem of controlling a tethered wing in this second phase, the retraction phase, is addressed here, by proposing two possible control strategies. Theoretical analyses, numerical simulations, and experimental results are presented to show the performance of the two approaches. Finally, the experimental results of complete autonomous power generation cycles are reported and compared with first-principle models.Comment: This manuscript is a preprint of a paper submitted for possible publication on the IEEE Transactions on Control Systems Technology and is subject to IEEE Copyright. If accepted, the copy of record will be available at IEEEXplore library: http://ieeexplore.ieee.or

Probabilistic performance validation of deep learning-based robust NMPC controllers

Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations. This challenge is even greater when uncertainty is present due to disturbances, unknown parameters or measurement and estimation errors. To enable the application of advanced control schemes to fast systems and on low-cost embedded hardware, we propose to approximate a robust nonlinear model controller using deep learning and to verify its quality using probabilistic validation techniques. We propose a probabilistic validation technique based on finite families, combined with the idea of generalized maximum and constraint backoff to enable statistically valid conclusions related to general performance indicators. The potential of the proposed approach is demonstrated with simulation results of an uncertain nonlinear system.gencia Estatal de Investigación (AEI)-Spain Grant PID2019-106212RB-C41/AEI/10.13039/501100011

Probabilistic performance validation of deep learning-based robust NMPC controllers

Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations. This challenge is even greater when uncertainty is present due to disturbances, unknown parameters or measurement and estimation errors. To enable the application of advanced control schemes to fast systems and on low-cost embedded hardware, we propose to approximate a robust nonlinear model controller using deep learning and to verify its quality using probabilistic validation techniques. We propose a probabilistic validation technique based on finite families, combined with the idea of generalized maximum and constraint backoff to enable statistically valid conclusions related to general performance indicators. The potential of the proposed approach is demonstrated with simulation results of an uncertain nonlinear system

Improved path following for kites with input delay compensation

In kite power systems, substantial input delay between the actuator and the tethered kite can severely hinder the performance of the control algorithm, limiting the capability of the system to track power-optimal loops. We propose a method that deals with this impediment by using a data-based adaptive filter that predicts future states despite variations in wind conditions, other exogenous disturbances and model mismatch. Moreover, we exploit the geometry of the path on a hemisphere to enhance the guidance algorithm for such kites at a fixed length tether. The objective is to improve the automatic crosswind operation of an airborne wind energy system. To test this under realistic conditions, a small-scale prototype was employed for a series of experiments. The robustness to disturbances and the performance of the algorithm in path following was evaluated for a number of different paths. © 2015 IEEE

Real-Time Optimization: Optimizing the Operation of Energy Systems in the Presence of Uncertainty and Disturbances

In practice, the quest for the optimal operation of energy systems is complicated by the simultaneous presence of operating constraints, among which the need for producing the power required by the user, and of uncertainty. The latter concept incorporates the potential inaccuracies of the models at hand but also degradation effects or unexpected changes, such as, e.g. random load changes or variations of the availability of the energy source for renewable energy systems. Since these changes affect the optimal values of the operating conditions, online adaptation is required to ensure that the system is always operated optimally. This typically implies the online solving of an optimization problem. Unfortunately, the applicability and the performances of most model-based optimization methods rely on the quality of the available model of the system under investigation. On the other hand, Real-Time Optimization (RTO) methods use the available online measurements in the optimization framework and are, thus, capable of bringing the desired self-optimizing control reaction. In this article, we show the benefits of using several RTO methods (co-) developed by the authors to energy systems through the successful application of (i) “Real-Time Optimization via Modifier Adaptation” to an experimental Solid Oxide Fuel Cells (SOFC) stack, of (ii) the recently released “SCFO-solver ” to an industrial SOFC stack, and of (iii) Dynamic RTO to a simulated tethered kite for renewable power production. It is shown how such problems can be formulated and solved and significant improvements of the performances of the three aforementioned energy systems are illustrated

Application of Real-Time Optimization Methods to Energy Systems in the Presence of Uncertainties and Disturbances

In practice, the quest for the optimal operation of energy systems is complicated by the presence of operating constraints, which includes the need to produce the power required by the user, and by the need to account for uncertainty. The latter concept incorporates the potential inaccuracies of the models at hand but also degradation effects or unexpected changes, such as, e.g. random load changes or variations of the availability of the energy source for renewable energy systems. Since these changes affect the optimal values of the operating conditions, online adaptation is required to ensure that the system is always operated optimally. This typically implies the online solving of an optimization problem. Unfortunately, the applicability and the performances of most model-based optimization methods rely on the quality of the available model of the system under investigation. On the other hand, Real-time optimization (RTO) methods use the available online measurements in the optimization framework and are, thus, capable of bringing the desired self-optimizing control reaction. In this article, we show the benefits of using several RTO methods (co-) developed by the authors to energy systems through the successful application of (i) "Real-Time Optimization via Modifier Adaptation" to an experimental solid oxide fuel cells (SOFC) stack, of (ii) the recently released "SCFO-solver" (where SCFO stands for “Sufficient Conditions of Feasibility and Optimality”) to an industrial SOFC stack, and of (iii) Dynamic RTO to a simulated tethered kite for renewable power production. It is shown how such problems can be formulated and solved, and significant improvements of the performances of the three aforementioned energy systems are illustrated