Linear Parameter Varying Power Regulation of Variable Speed Pitch Manipulated Wind Turbine in the Full Load Regime

In a wind energy conversion system (WECS), changing the pitch angle of the wind turbine blades is a typical practice to regulate the electrical power generation in the full-load regime. Due to the turbulent nature of the wind and the large variations of the mean wind speed during the day, the rotary elements of the WECS are subjected to significant mechanical stresses and fatigue, resulting in conceivably mechanical failures and higher maintenance costs. Consequently, it is imperative to design a control system capable of handling continuous wind changes. In this work, Linear Parameter Varying (LPV) H_inf controller is used to cope with wind variations and turbulent winds with a turbulence intensity greater than 10%. The proposed controller is designed to regulate the rotational rotor speed and generator torque, thus, regulating the output power via pitch angle manipulations. In addition, a PI-Fuzzy control system is designed to be compared with the proposed control system. The closed-loop simulations of both controllers established the robustness and stability of the suggested LPV controller under large wind velocity variations, with minute power fluctuations compared to the PI-Fuzzy controller. The results show that in the presence of turbulent wind speed variations, the proposed LPV controller achieves improved transient and steady-state performance along with reduced mechanical loads in the above-rated wind speed region.Comment: 12 pages, 10 figure

Closed-loop separation control over a sharp edge ramp using Genetic Programming

We experimentally perform open and closed-loop control of a separating turbulent boundary layer downstream from a sharp edge ramp. The turbulent boundary layer just above the separation point has a Reynolds number $Re_{\theta}\approx 3\,500$ based on momentum thickness. The goal of the control is to mitigate separation and early re-attachment. The forcing employs a spanwise array of active vortex generators. The flow state is monitored with skin-friction sensors downstream of the actuators. The feedback control law is obtained using model-free genetic programming control (GPC) (Gautier et al. 2015). The resulting flow is assessed using the momentum coefficient, pressure distribution and skin friction over the ramp and stereo PIV. The PIV yields vector field statistics, e.g. shear layer growth, the backflow area and vortex region. GPC is benchmarked against the best periodic forcing. While open-loop control achieves separation reduction by locking-on the shedding mode, GPC gives rise to similar benefits by accelerating the shear layer growth. Moreover, GPC uses less actuation energy.Comment: 24 pages, 24 figures, submitted to Experiments in Fluid

Model-based robust H∞ control of a granulation process using Smith predictor with Reference updating

International audienceModel-based feedback control is developed for a continuous granulation process addressing the challengeof time delay and physics-based input-output constraints. The process plant is a multi-input multi-output (MIMO) linear model with time delay. A robust H ∞ controller is designed using the mixed sensitivity loop shaping design. A framework has been laid down to insure the robustness of the Smith predictor by incorporating the model mismatch as an additive uncertainty in the predictor’s structure. The control performance and robustness is assessed by simulations for regulation and reference tracking problems. We show significant performance gains by employing a Smith predictor and the technique of reference updating: The control is coping significantly better with time delay, physical constraints and model mismatch. The proposed control approach is more efficient as compared to other widely used methods such as model predictive control (MPC); obtaining a stable behaviour of the response and control effort while forcing them to remain within the desired bounds