A Bayesian optimization approach for wind farm power maximization

Abstract

The objective of this study is to develop a model-free optimization algorithm to improve the total wind farm power production in a cooperative game framework. Conventionally, for a given wind condition, an individual wind turbine maximizes its own power production without taking into consideration the conditions of other wind turbines. Under this greedy control strategy, the wake formed by the upstream wind turbine, due to the reduced wind speed and the increased turbulence intensity inside the wake, would affect and lower the power productions of the downstream wind turbines. To increase the overall wind farm power production, researchers have proposed cooperative wind turbine control approaches to coordinate the actions that mitigate the wake interference among the wind turbines and thus increase the total wind farm power production. This study explores the use of a data-driven optimization approach to identify the optimum coordinated control actions in real time using limited amount of data. Specifically, we propose the Bayesian Ascent (BA) method that combines the strengths of Bayesian optimization and trust region optimization algorithms. Using Gaussian Process regression, BA requires only a few number of data points to model the complex target system. Furthermore, due to the use of trust region constraint on sampling procedure, BA tends to increase the target value and converge toward near the optimum. Simulation studies using analytical functions show that the BA method can achieve an almost monotone increase in a target value with rapid convergence. BA is also implemented and tested in a laboratory setting to maximize the total power using two scaled wind turbine models.