Nacelle anemometer measurement‐based extremum‐seeking wind turbine region‐2 control for improved convergence in fluctuating wind

Abstract

For region‐2 operation of wind turbines in practice, the optimal torque gain can deviate from the nominal value because of the variations in turbine and wind conditions. The extremum‐seeking control (ESC) has shown its potential as a model‐free region‐2 control solution in some recent work; however, the ESC with rotor power feedback suffers from undesirable convergence under fluctuating wind. In this paper, we propose to use an estimated power coefficient as the objective function for the torque‐gain ESC, where the hub‐height free‐stream wind speed (FSWS) is estimated with the nacelle anemometer measurement on the basis of the so‐called nacelle transfer function (NTF) between the nacelle anemometer and met‐tower measurement. A sensitivity analysis is performed to quantify the impact of the wind speed estimation error on the estimation of power coefficient. An ESC integrated interregion switching scheme is proposed to avoid the load increase. Simulation results show that, compared with the power feedback‐based ESC, the proposed method can greatly improve the convergence rate of ESC under fluctuating wind, even under relatively large wind speed estimation error. Evaluation for the fatigue loads of wind turbine shows that the proposed control strategy induces mild increase of the wind turbine load.

A Study on Available Power Estimation Algorithm and Its Validation

“…There also exist other simple methods to estimate the available power of a wind turbine without using machine learning techniques. One of the simplest methods is to use the wind speed measured directly from the anemometer that is normally installed on top of the nacelle of a wind turbine or use nacelle transfer function model [29]. The power curve of the wind turbine can be used with the measured wind speed to estimate the available power [30].…”

Adaptive Extremum Seeking Control of Urban Area Wind Turbines

“…In [12], the sliding mode extremum seeking approach was generalized to nonlinear systems. In [13], issues of undesirable convergence under fluctuating wind of extremum seeking were addressed by using an estimated power coefficient obtained by an additional wind speed nacelle anemometer measurement in combination with extremum seeking. In [14], a model-based recursive Gaussian process approach was developed for a lighter-than-air wind energy conversion systems and was compared with extremum seeking.…”