The efficiency of horizontal axis wind turbines greatly depends on the efficient utilization of the aerodynamic lift force. The coefficient of lift could be altered using the flow control techniques. The dielectric barrier discharge (DBD)-based plasma actuator is an evolving active flow control technique in which the moment induced in air by DBD plasma activation improves the power capturing capacity of the blades without any mechanical actuation. But due to the offset plasma actuation in extreme wind condition, it would induce structural stress on turbine components. The effect of optimized plasma actuation by varying the voltage amplitude and with a fixed frequency of 24 kHz is to be analyzed. The duty cycle of the plasma actuator is maintained constant at 90%. The optimal point plasma operation would ensure the maximum power production and reduced bending moment of blades. The optimum plasma activation also reduces the power consumption of the actuator. The instantaneous optimum Pareto best is obtained by recursive identification of dynamic system response using non-dominated sorting genetic algorithm 2. The converging points of the algorithm are validated using a NREL FAST 5MW offshore baseline wind turbine model interfaced with MATLAB.
The concept of ‘Smart Rotor’ is an evolving advancement in wind turbine which enables an intelligent Active flow control in rotor. The Deformable trailing edge flap (DTEF) is a part of smart rotor concept which implements a customized active load control. The trailing edge flap actuator effectively replaces the tedious blade pitch actuation and conserves the actuation energy required for pitching the entire blade. The DTEFs require a fast computing, anticipatory controller for optimally tuning the flap angle with minimal power compromise. This work analyses the performance of advanced control strategies like Model predictive control (MPC), Adaptive MRAC control and DQ controllers. The MRAC controller is found to reduce the fatigue stress by 40% and the MPC controller damps up to 70% more efficiently than the typical feedback controller. The Control strategies are aided by the LiDAR based preview wind data for the active manipulation of trailing edge flap angle \(\left({\theta }_{flap}\right)\) control. The validation of proposed controller is done using power analysis curve and the components fatigue lifetime analysis using MLIFE software. The above analyses are done in NREL Onshore 5 MW FAST wind turbine model which could be interfaced with MATLAB with modified AeroDyn code for active flap deflection.
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