Heteroscedastic Bayesian Optimisation for Active Power Control of Wind Farms*

Abstract

Active power control of wind farms remains an open challenge due to inherent noise in wind power that arises from uncertain wind speed measurements and plant/model mismatch. To leverage the heteroscedastic nature of the wind power noise, heteroscedastic Bayesian optimisation (BO) is used for active power control of wind farms. BO utilises closed-loop performance data to tune the parameters of a stochastic model predictive controller (SMPC) in a systematic and data-efficient manner. This, in turn, allows for enhancing the closed-loop performance of the controller intended to decrease the power tracking error. A case study with 9 turbines in a 3 × 3 wind farm shows that the heteroscedastic BO approach achieves a reduced closed-loop power tracking error in terms of root-mean-square by 8.89% compared to one that relies on nominal BO and a decrease by 64.99% compared to a nominal model predictive controller (MPC) whose performance is not tuned using closed-loop data and BO.