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)
- Institution
- Western Carolina University (WCU )
- Web Site: http://library.wcu.edu/
- Advisor
- 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.
Slider crank wave energy converter performance analysis with adaptive autoregressing filtering
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Created on 4/1/2019
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Additional Information
- Publication
- Thesis
- Language: English
- Date: 2019
- Subjects
- Ocean wave power
- Energy conversion
- Electric current converters
- Adaptive filters
- Ocean energy resources
- Renewable energy sources