Non-linear Model Predictive Control of Wave Energy Converters with Realistic Power Take-off Configurations and Loss Model

Journal Article · Thu Aug 01 00:00:00 EDT 2019 · IEEE Conference on Control Technology and Applications (CCTA)

DOI:https://doi.org/10.1109/ccta.2019.8920640· OSTI ID:1856262

Karthikeyan, A. ^[1]; Previsic, M. ^[2]; Scruggs, J. ^[3]; Chertok, A. ^[4]

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1. Re Vision Consulting, LLC, Sacramento, CA (United States); Re Vision Consulting
2. Re Vision Consulting, LLC, Sacramento, CA (United States)
3. Univ. of Michigan, Ann Arbor, MI (United States)
4. Resolute Marine Energy, Boston, MA (United States)

Model Predictive Control (MPC) has been recognized as a well-developed control strategy for optimizing the performance of wave energy converters (WECs). The standard problem involves maximizing the absorbed power from oncoming waves while respecting motion constraints of the device and force constraints of the power take-off (PTO). The generated electrical power is then calculated by assuming an ideal PTO with no power conversion losses or limitations to power flow. In this article, we remove the assumption of an ideal PTO and present four different control options that reflect unique combinations of PTO control and power flow constraints. We also recast the MPC problem to maximize the generated power instead of the absorbed power by accounting for the losses in the power train. Using a WEC with a hydraulic PTO, we develop a loss model suitable for controls optimization. Lastly, we illustrate the process of setting up the non-linear MPC problem and compare the performance of competing control strategies using a common benchmark based on annual energy captured at a reference site.

• Research Organization: Re Vision Consulting, LLC, Sacramento, CA (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office
• Grant/Contract Number: EE0007173; EE0008099
• OSTI ID: 1856262
• Journal Information: IEEE Conference on Control Technology and Applications (CCTA), Journal Name: IEEE Conference on Control Technology and Applications (CCTA) Vol. 2019; ISSN 2768-0770
• Publisher: IEEE
• Country of Publication: United States
• Language: English

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