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An efficient strategy for maximizing the power production of a power plant is to control in a coordinated way only turbines that are aerodynamically coupled through wake effects. The implementation of such control strategy requires the knowledge of which clusters of turbines are coupled through wake interaction. In a previous study, we identified turbine clusters in real-time by evaluating the correlation among the power production signals of the turbines in the farm. In this study we reproduce the more challenging scenario with large scale variation of the wind direction. Different time windows of data needed to compute the correlation coefficients are tested and characterized in term of accuracy and promptness of the identification. 

This paper presents regression and classification methods to estimate wind direction in a wind farm from operational data. Two neural network models are trained using supervised learning. The data are generated using high-fidelity large eddy simulations (LES) of a virtual wind farm with 16 turbines, which are representative of the data available in actual SCADA systems. The simulations include the high-fidelity flow physics and turbine dynamics. The LES data used for training and testing the neural network models are the rotor angular speeds of each turbine. Our neural network models use sixteen angular speeds as inputs to produce an estimate of the wind direction at each point in time. Training and testing of the neural network models are done for seven discrete wind directions, which span the most interesting cases due to symmetry of the wind farm layout. The results of this paper are indicative of the potential that existing neural network models have to obtain estimates of wind direction in real time. 

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. 

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.

 

The extremum seeking control (ESC) algorithm has been proposed to determine operating parameters that maximize power production below rated wind speeds (region II). This is usually done by measuring the turbine's power signal to determine optimal values for parameters of the control law or actuator settings. This paper shows that the standard ESC with power feedback is quite sensitive to variations in mean wind speed, with long convergence time at low wind speeds and aggressive transient response, possibly unstable, at high wind speeds. The paper also evaluates the performance, as measured by the dynamic and steady state response, of the ESC with feedback of the logarithm of the power signal (LP‐ESC). Large eddy simulations (LES) demonstrate that the LP‐ESC, calibrated at a given wind speed, exhibits consistent robust performance at all wind speeds in a typical region II. The LP‐ESC is able to achieve the optimal set‐point within a prescribed settling time, despite variations in the mean wind speed, turbulence, and shear. The LES have been conducted using realistic wind input profiles with shear and turbulence. The ESC and LP‐ESC are implemented in the LES without assuming the availability of analytical gradients.