Advanced Log-of-power Extremum Seeking Control for Wind Power Maximization

The emergence of wind power generation as one of the most economic and viable forms of renewable energy depends on the reduction of the Levelized Cost of Energy (LCOE). Advanced control strategies designed to increase wind power capture and decrease the structural loads could play a vital role in reducing the LCOE. This dissertation research aims to investigate a real-time model-free control strategy, the Logarithm of Power Extremum Seeking Control (LP-ESC), to increase the energy capture by optimizing wind turbine control parameters such as generator torque gain, tip-speed ratio set point, blade pitch angle, and nacelle yaw angle. LP-ESC can optimally tune these control parameters in off-design conditions without detailed knowledge of the underlying dynamics or the environment. The basic ESC algorithm suffers from slow and inconsistent convergence under changing wind speeds in below-rated conditions. Although the inconsistent convergence of the basic ESC algorithm can be handled by changing the performance index from the power signal to its logarithm, i.e., by using the LP-ESC, the algorithm can still show a slow convergence in some applications. In the current work, we propose a Log-of-Power Proportional-Integral ESC (LP-PIESC) to accelerate the convergence of the LP-ESC algorithm. The first part of the research involves design and simulation of a real-time weighted multiobjective Log-of-Power ESC (LP-ESC) scheme to maximize the wind turbine power capture with load reduction. The next part is focused on the design and simulation study of the LPESC and the LP-PIESC (Log-of-Power PIESC) on an NREL 5MW reference wind turbine model in NREL’s OpenFAST code. Torque gain and blade pitch angle are used as the tuning parameters. Results indicate that the LP-PIESC is much faster in finding the unknown optimum, leading to a more practical algorithm for real applications. To demonstrate the performance of the LP-PIESC under blade degradation and contamination, the algorithm is used to re-tune the optimal tip speed ratio (TSR) for a turbine using the recently proposed NREL’s Reference Open-Source Controller (ROSCO). It is shown that the LP-PIESC can determine the new optimal TSRs when there are changes in aerodynamic parameters due to blade degradation and contamination. This dissertation also describes the results from wind tunnel experiments performed to maximize wind plant total power output using wake steering via closed-loop yaw angle control. The experimental wind plant consists of nine turbines arranged in two different layouts, both are two-dimensional arrays and differ in the positioning of the individual turbines. Both algorithms are implemented to maximize wind plant power: LP-ESC and LP-PIESC. These algorithms command the yaw angles of the turbines in the upstream row. The results demonstrate that the algorithms can find the optimal yaw angles that maximize total power output. The LP-PIESC reached the optimal yaw angles much faster than the LP-ESC. The sensitivity of the LP-PIESC to variations in free stream wind speed and initial yaw angles is studied to demonstrate robustness to variations in wind speed and unknown yaw misalignment. Finally, the LP-PIESC algorithm is also tested experimentally on a complex terrain consisting of eight scaled wind turbines. The complex terrain was modeled by scaling a real wind farm to fit in the wind tunnel. The LP-PIESC converged almost 30 times faster than the LP-ESC. Results show that the LP-PIESC could find the unknown optimal yaw angles even in the presence of gusty winds.