ALK-PE

An efficient active learning Kriging approach for wave energy converter power matrix estimation

Journal article (2023)

Authors

Chao Ren University of Stavanger
J. Tan Offshore and Dredging Engineering - Mechanical, Maritime and Materials Engineering
Yihan Xing University of Stavanger
Research Group
Offshore and Dredging Engineering (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 Chao Ren, J. Tan, Yihan Xing
DOI
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https://doi.org/10.1016/j.oceaneng.2023.115566
Active learning Wave energy converter Kriging surrogate model WEC power matrix estimation
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Published Date

2023

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Offshore and Dredging Engineering

Abstract

Wave energy is considered one of the most potential renewable energy. In the last two decades, many wave energy converters (WECs) have been designed to harvest energy from the ocean. Different power take-off systems are developed to maximize the power generation of WECs. However, the estimation of the power matrix of the WECs and annual power generation on the different sites is much more complex. A lot of simulations or experiments are required to obtain the power matrix of one specific WEC. To solve this problem, this paper proposes an active learning Kriging approach to estimate the WEC power matrix with less computational cost or experiment test. The efficiency of the proposed approach is demonstrated by two analytic problems and a point absorber WEC. The results show the proposed approach can efficiently and accurately estimate the power matrix of the WECs. Using the proposed ALK-PE approach, less than one-fifth of simulations or experiments are required to construct the whole power matrix of WECs at all the sea states, and the mean absolute percentage error is around 1%.