ALK-PE: An efficient active learning Kriging approach for wave energy converter power matrix estimation

Chao Ren, Jian Tan*, Yihan Xing

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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%.

Original languageEnglish
Article number115566
Number of pages12
JournalOcean Engineering
Volume286
DOIs
Publication statusPublished - 2023

Keywords

  • Active learning
  • Kriging surrogate model
  • Wave energy converter
  • WEC power matrix estimation

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