Real-Time Control and Optimal Plant Design for Renewable Energy Systems Using Bayesian Optimization

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Baheri, A. (2018). Real-Time Control and Optimal Plant Design for Renewable Energy Systems Using Bayesian Optimization. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

The overarching objective of this research is to use machine learning tools to address the design and real-time control of active systems, focusing specifically by tethered airborne wind energy and ocean current energy systems. In both applications, along with numerous other engineering applications, data is expensive to generate. In particular, generating new plant designs is costly, and any adjustments to the controller must be performed in an environment that is continually changing. To address these challenges, we leverage a data-driven optimization strategy called Bayesian Optimization, which is specifically tailored to optimization problems for which a model does not exist (necessitating expensive data collection) or must be controlled with expensive experiments. This dissertation extends the current state of the art in Bayesian Optimization to enable Bayesian Optimization to be performed in real-time, in a spatiotemporally-varying environment. The techniques described in this dissertation have been applied using real-data, to the real-time altitude and depth optimization of airborne wind and ocean current turbine energy systems, respectively. Furthermore, the techniques have been applied to the nested co-design (combined plant/controller design) of an airborne wind energy system.

Details
Author

Baheri, Ali

Title

Real-Time Control and Optimal Plant Design for Renewable Energy Systems Using Bayesian Optimization

Physical Description

1 online resource (119 pages) : PDF

Date

2018

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

The overarching objective of this research is to use machine learning tools to address the design and real-time control of active systems, focusing specifically by tethered airborne wind energy and ocean current energy systems. In both applications, along with numerous other engineering applications, data is expensive to generate. In particular, generating new plant designs is costly, and any adjustments to the controller must be performed in an environment that is continually changing. To address these challenges, we leverage a data-driven optimization strategy called Bayesian Optimization, which is specifically tailored to optimization problems for which a model does not exist (necessitating expensive data collection) or must be controlled with expensive experiments. This dissertation extends the current state of the art in Bayesian Optimization to enable Bayesian Optimization to be performed in real-time, in a spatiotemporally-varying environment. The techniques described in this dissertation have been applied using real-data, to the real-time altitude and depth optimization of airborne wind and ocean current turbine energy systems, respectively. Furthermore, the techniques have been applied to the nested co-design (combined plant/controller design) of an airborne wind energy system.

Genre

doctoral dissertations

Subjects--Topics

Mechanical engineering
Computer science

Degree

Ph.D.

Keywords

Airborne Wind Energy
Bayesian Optimization
Co-Design
Ocean Current Turbine
Optimization
Wind Energy

Subject Area

Mechanical Engineering

Advisor(s)

Vermillion, Christopher

Committee Members

Ramaprabhu, Praveen
Kelly, Scott
Williams, Wesley
Goudarzi, Navid

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2018.

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Rights Holder Information

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Identifier

Baheri_uncc_0694D_11789

Permalink

http://hdl.handle.net/20.500.13093/etd:1690