A trajectory-based sampling strategy for sequentially refined metamodel management of metamodel-based dynamic optimization in mechatronics

Dynamic optimization problems based on computationally expensive models that embody the dynamics of a mechatronic system can result in prohibitively long optimization runs. When facing optimization problems with static models, reduction in the computational time and thus attaining convergence can be established by means of a metamodel placed within a metamodel management scheme. This paper proposes a metamodel management scheme with a dedicated sampling strategy when using computationally demanding dynamic models in a dynamic optimization problem context. The dedicated sampling strategy enables to attain dynamically feasible solutions where the metamodel is locally refined during the optimization process upon satisfying a feasibility-based stopping condition. The samples are distributed along the iterate trajectories of the sequential direct dynamic optimization procedure. Algorithmic implementation of the trajectory-based metamodel management is detailed and applied on two case studies involving dynamic optimization problems. These numerical experiments illustrate the benefits of the presented scheme and its sampling strategy on the convergence properties. It is shown that the acceleration of the solution time of the dynamic optimization problem can be achieved when evaluating the metamodel that is lower than 90% compared to the computationally expensive model

Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method

Predictive control of wind farms based on lexicographic minimizers for power reserve maximization

Trabajo presentado en la Annual American Control Conference (ACC), celebrada en Milwaukee (WI, USA), del 27 al 29 de junio de 2018This paper presents a model predictive control (MPC) strategy aimed to regulate the total power delivered to the grid while maximizing the power reserve. Nowadays, the high participation of wind energy in the electricity generation requires that wind power plants (WPPs) also provide ancillary services. This fact implies that WPPs must be capable of temporally increasing the power generation to help, for instance, the primary-frequency control. To this end, WPPs work below the maximum generation capacity keeping some power reserves (difference between available and generated powers). The available power depends on the wind conditions that each turbine is facing but these conditions are also affected by the wakes produced by upstream turbines. In order to satisfy the aforementioned objectives, this work proposes to cast the MPC strategy as a multi-objective optimization problem solved using a lexicographic approach in order to consider the hierarchy of the control objectives. The performance of the control scheme is evaluated by simulations for the case of a WPP with three turbines taking into account the variation of wind speed faced by downstream turbines due to the wake effect.Peer reviewe

C-V characterization technique for four-terminal GaN-on-Si HEMTs based on 3-port S-parameter measurements

This paper presents a low complexity measurement technique to characterize state-of-the-art GaN-on-SiHEMTs based on 3-port S-parameter measurements. The proposed measurement technique permits the C-V characterization of the six inter-electrode capacitances (CGS, CGD, CDS, CBS, CBG, CBD) inherent in a 4-terminal GaN-on-Si HEMT up to 1 kV using a single low-budget test fixture. A-state-of-the-art GaN-on-Si power HEMT available in the market is used as DUT to validate the proposed measurement technique. Measurements show a good agreement with data sheet values and transistor model simulations

Distributed Low-Complexity Controller for Wind Power Plant in Derated Operation

We consider a wind power plant of megawatt wind turbines operating in derated mode. When operating in this mode, the wind power plant controller is free to distribute power set-points to the individual turbines, as long as the total power demand is met. In this work, we design a controller that exploits this freedom to reduce the fatigue on the turbines in the wind power plant. We show that the controller can be designed in a decentralized manner, such that each wind turbine is equipped with a local low-complexity controller relying only on few measurements and little communication. As a basis for the controller design, a linear wind turbine model is constructed and verified in an operational wind power plant of megawatt turbines. Due to limitations of the wind power plant available for tests, it is not possible to implement the developed controller; instead the final distributed controller is evaluated via simulations using an industrial wind turbine model. The simulations consistently show fatigue reductions in the magnitude of 15 – 20 %