Slider Crank WEC Performance Analysis with Adaptive Autoregressive Filtering

Md Khan, H. Karayaka, Yanjun Yan, Peter Tay, Yi-Hsiang Yu

Research output: Contribution to conferencePaper

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 15 th order filter with moderate prediction horizon length.
Original languageAmerican English
Number of pages6
StatePublished - 2020
Event2019 SoutheastCon - Huntsville, Alabama
Duration: 11 Apr 201914 Apr 2019


Conference2019 SoutheastCon
CityHuntsville, Alabama

NREL Publication Number

  • NREL/CP-5000-77163


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


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