Slider crank wave energy converter performance analysis with adaptive autoregressing filtering

WCU Author/Contributor (non-WCU co-authors, if there are any, appear on document)
Md. Rakib Hasan Khan (Creator)
Western Carolina University (WCU )
Web Site:
Bora Karayaka

Abstract: This study 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 into near future 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 in noisy and noiseless environment. 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.

Additional Information

Language: English
Date: 2019
Ocean wave power
Energy conversion
Electric current converters
Adaptive filters
Ocean energy resources
Renewable energy sources

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