Slider Crank WEC Performance Analysis with Adaptive Autoregressive Filtering

Md Rakib Hasan Khan, H. Bora Karayaka, Yanjun Yan, Peter Tay, Yi Hsiang Yu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


This paper investigates a performance analysis of wave excitation force prediction to extract wave power for a slider crank power take-off system (PTOS) based on auto regressive (AR) filters. To efficiently convert wave energy into electricity, the prediction of wave excitation forces to keep the generator and the wave excitation force in sync is important for maximum energy extraction. The study shows a prediction methodology of half period and zero crossings in the practical scenario of irregular ocean waves. The prediction has been tested for different wave periods and with different filter orders. The prediction results have been used in the PTOS simulation to analyze the energy extraction. It has been shown that the prediction accuracy in the wave half period between the truth data and the predicted data drives the WEC energy extraction efficiency. The amplitude of the wave force is not used and hence the prediction deviation in the wave force amplitude does not affect the PTOS energy extraction. Further analysis shows that the optimum energy can be extracted at 15th order filter with moderate prediction horizon length.

Original languageEnglish
Title of host publication2019 IEEE SoutheastCon, SoutheastCon 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728101378
StatePublished - Apr 2019
Event2019 IEEE SoutheastCon, SoutheastCon 2019 - Huntsville, United States
Duration: 11 Apr 201914 Apr 2019

Publication series

NameConference Proceedings - IEEE SOUTHEASTCON
ISSN (Print)0734-7502


Conference2019 IEEE SoutheastCon, SoutheastCon 2019
Country/TerritoryUnited States


  • autoregressive filter
  • prediction
  • slider-crank
  • wave energy converter
  • wave excitation force


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