This work 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. Two algorithms are implemented to maximize wind plant power: Log‐of‐Power Extremum Seeking Control (LP‐ESC) and Log‐of‐Power Proportional Integral Extremum Seeking Control (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.

This paper proposes a Log-Power Proportional-Integral Extremum Seeking Control (LP-PIESC) framework for maximizing the power capture of a wind turbine operating at below-rated wind speeds, i.e., the so-called region-2 of a turbine’s power curve. Extremum seeking control (ESC) has emerged as a viable algorithm to maximize energy capture for a wind turbine operating in region-2. Despite the encouraging results of early ESC strategies, the basic algorithm suffers from slow and inconsistent convergence behavior under changing wind speed within region-2. It has been shown that replacing the power signal with its logarithm results in an algorithm that is robust and predictable even when the mean wind speed varies. In addition, new studies have suggested that replacing conventional ESC with proportional plus integral ESC (PIESC) results in faster convergence to optimal conditions. In the current paper, the idea of log-power feedback is merged with the PIESC scheme and is applied to tune the parameters of the region-2 torque controller for the NREL 5-MW turbine reference model. The results of this new algorithm are compared with the ESC with log-of-power feedback using NREL OpenFAST simulations. The log-power feedback PIESC is also implemented for the blade pitch set-point angle. Energy capture over the course of the simulations and damage equivalent loads calculated with MLife are used to assess the results. The simulations performed under different turbulent intensity cases demonstrate the rapid convergence of the log-power feedback PIESC.

Wind tunnel experiments were performed to quantify the coupling mechanisms between incoming wind flows, power output fluctuations, and unsteady tower aerodynamic loads of a model wind turbine under periodically oscillating wind environments across various yaw misalignment angles. A high-resolution load cell and a data logger at high temporal resolution were applied to quantify the aerodynamic loads and power output, and time-resolved particle image velocimetry system was used to characterize incoming and wake flow statistics. Results showed that due to the inertia of the turbine rotor, the time series of power output exhibits a distinctive phase lag compared to the incoming periodically oscillating wind flow, whereas the phase lag between unsteady aerodynamic loads and incoming winds was negligible. Reduced-order models based on the coupling between turbine properties and incoming periodic flow characteristics were derived to predict the fluctuation intensity of turbine power output and the associated phase lag, which exhibited reasonable agreement with experiments. Flow statistics demonstrated that under periodically oscillating wind environments, the growth of yaw misalignment could effectively mitigate the overall flow fluctuation in the wake region and significantly enhance the stream-wise wake velocity cross correlation intensities downstream of the turbine hub location.

Rotor performance is adversely affected by the wear and tear of blade surfaces caused, for example, by rain, snow, icing, dirt, bugs and aging. Blade surface degradation changes the aerodynamic properties of the rotor, which in turn changes the optimal tip-speed ratio (TSR) and the corresponding maximum power coefficient. Below the rated wind speed, if a turbine continues to operate at the manufacturer-designed optimal TSR, the rotor power could decrease more than necessary unless the optimal TSR is corrected to compensate for blade degradation or blade surfaces are restored. Re-tuning the tip-speed ratio can lead to an improvement in energy capture without blade repairs. In this work, we describe a real-time algorithm to re-tune the tip-speed ratio to its optimal but unknown value under blade degradation. The algorithm uses power measurements only and the Log-Power Proportional-Integral Extremum Seeking Control (LP-PIESC) strategy to re-tune the TSR. The algorithm is demonstrated in simulations to command the set-point TSR required by a generator speed control loop that maximizes power at below-rated wind speeds. Comparison of this solution with a baseline controller that uses the optimal TSR for a rotor with clean blades demonstrates improvements in energy capture between 0.5 % and 3.4 %, depending on the severity of blade degradation and the wind conditions. These results are obtained using the OpenFAST simulation tool, the National Renewable Energy Laboratory (NREL) 5 MW reference turbine and the NREL-developed Reference Open-Source Controller (ROSCO).

In this paper, we propose a power-setpoint based Extremum Seeking Control (ESC) framework for model-free Region-2 controls for maximizing the power capture for turbine and farm operation, without dependency on wind measurement. As a major obstacle for retrofitting wind turbine/farm controls is that only the power setpoint is accessible, the power-setpoint based ESC framework is proposed with a back-calculation anti-windup structure. If increasing the power demand cannot further increase actual power output, the anti-windup structure automatically holds the power demand setpoint. For farm operation, the proposed method is integrated into the Delay-compensated Nested-loop ESC. The proposed method is evaluated by simulations on the SimWindFarm platform for both single-turbine and farm operation scenarios. The results demonstrate the capability of tracking the achievable optimum power for turbine and farm operation, with only reasonable increase of some loads. The proposed method promises an easy-to-implement model-free retrofitting control strategy for enhancing wind energy capture.

This paper describes a multi-objective ESC strategy that determines Pareto-optimal control parameters to jointly optimize wind turbine loads and power capture. The method uses two optimization objectives calculated in real time: (a) the logarithm of the average power and (b) the logarithm of the standard deviation of a measurable blade load, tower load or the combination of these loads. These two objectives are weighted in real-time to obtain a solution that is Pareto optimal with respect to the power average and the standard deviation of chosen load metric. The method is evaluated using NREL FAST simulations of the 5-MW reference turbine. The results are then evaluated using energy capture over the duration of the simulation and damage equivalent loads (DEL) calculated with MLife.