Contextual Bayesian optimization with trust region (CBOTR) and its application to cooperative wind farm control in region 2

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In this study, we propose a contextual Bayesian optimization with Trust-Region (CBOTR), an extended version of Bayesian optimization (BO) that can find an optimum input of a target system (or unknown function) through the iterative learning and sampling procedure. CBOTR adds two features to BO: (1) CBOTR can take into account context information which modifies the input and output relationship of a target system, and (2) CBOTR restricts the searching space for the next input to be selected so that it can rapidly find an optimum. The results from simulation studies using a set of benchmark functions and a wind farm power simulator showed that the CBOTR algorithm can achieve an almost optimum target value by taking a small number of trial actions (samplings). The proposed algorithm particularly suits well to determine the joint optimal operational conditions of wind turbines in a wind farm for maximizing the total energy production, in that the complex interaction among wind turbines in a wind farm is difficult to model using an analytical model and one needs to find the optimum operational conditions for varying wind conditions.
Publisher
ELSEVIER
Issue Date
2020-04
Language
English
Article Type
Article
Citation

SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, v.38

ISSN
2213-1388
DOI
10.1016/j.seta.2020.100679
URI
http://hdl.handle.net/10203/275056
Appears in Collection
IE-Journal Papers(저널논문)
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