Wave Excitation Force Prediction Methodology Based on Autoregressive Filters for Real Time Control

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

doi:10.1109/GreenTech.2019.8767127

Wave Excitation Force Prediction Methodology Based on Autoregressive Filters for Real Time Control

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

Department of Civil Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

This paper presents a wave excitation force prediction methodology for a slider crank power take-off system (PTOS) based on autoregressive filters. This study shows the feasibility of half period and zero crossing prediction of the irregular ocean waves to keep phase lock condition between the generator and the wave excitation force. This is critically important for unidirectional continuous rotation of the generator to maximize energy extraction. Results from the prediction with different filter orders are analyzed to accommodate real time processing requirements. It has been shown that 10^th order filter provides the good compromise of accuracy and timely processing.

Original language	English
Title of host publication	2019 IEEE Green Technologies Conference, GreenTech 2019
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728114576
DOIs	https://doi.org/10.1109/GreenTech.2019.8767127
State	Published - Apr 2019
Event	2019 IEEE Green Technologies Conference, GreenTech 2019 - Lafayette, United States Duration: 3 Apr 2019 → 6 Apr 2019

Publication series

Name	IEEE Green Technologies Conference
Volume	2019-April
ISSN (Electronic)	2166-5478

Conference

Conference	2019 IEEE Green Technologies Conference, GreenTech 2019
Country/Territory	United States
City	Lafayette
Period	3/04/19 → 6/04/19

Keywords

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

Access to Document

10.1109/GreenTech.2019.8767127

Cite this

Hasan Khan, M. R., Karayaka, H. B., Yan, Y., Tay, P., & Yu, Y. H. (2019). Wave Excitation Force Prediction Methodology Based on Autoregressive Filters for Real Time Control. In 2019 IEEE Green Technologies Conference, GreenTech 2019 Article 8767127 (IEEE Green Technologies Conference; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/GreenTech.2019.8767127

@inproceedings{2e879318dff342f2b6a2bdde71ef2017,

title = "Wave Excitation Force Prediction Methodology Based on Autoregressive Filters for Real Time Control",

abstract = "This paper presents a wave excitation force prediction methodology for a slider crank power take-off system (PTOS) based on autoregressive filters. This study shows the feasibility of half period and zero crossing prediction of the irregular ocean waves to keep phase lock condition between the generator and the wave excitation force. This is critically important for unidirectional continuous rotation of the generator to maximize energy extraction. Results from the prediction with different filter orders are analyzed to accommodate real time processing requirements. It has been shown that 10th order filter provides the good compromise of accuracy and timely processing.",

keywords = "autoregressive filter, prediction, slider-crank, wave energy converter, wave excitation force",

author = "{Hasan Khan}, {Md Rakib} and Karayaka, {H. Bora} and Yanjun Yan and Peter Tay and Yu, {Yi Hsiang}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Green Technologies Conference, GreenTech 2019 ; Conference date: 03-04-2019 Through 06-04-2019",

year = "2019",

month = apr,

doi = "10.1109/GreenTech.2019.8767127",

language = "English",

series = "IEEE Green Technologies Conference",

publisher = "IEEE Computer Society",

booktitle = "2019 IEEE Green Technologies Conference, GreenTech 2019",

address = "美國",

}

Hasan Khan, MR, Karayaka, HB, Yan, Y, Tay, P & Yu, YH 2019, Wave Excitation Force Prediction Methodology Based on Autoregressive Filters for Real Time Control. in 2019 IEEE Green Technologies Conference, GreenTech 2019., 8767127, IEEE Green Technologies Conference, vol. 2019-April, IEEE Computer Society, 2019 IEEE Green Technologies Conference, GreenTech 2019, Lafayette, United States, 3/04/19. https://doi.org/10.1109/GreenTech.2019.8767127

Wave Excitation Force Prediction Methodology Based on Autoregressive Filters for Real Time Control. / Hasan Khan, Md Rakib; Karayaka, H. Bora; Yan, Yanjun et al.
2019 IEEE Green Technologies Conference, GreenTech 2019. IEEE Computer Society, 2019. 8767127 (IEEE Green Technologies Conference; Vol. 2019-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Wave Excitation Force Prediction Methodology Based on Autoregressive Filters for Real Time Control

AU - Hasan Khan, Md Rakib

AU - Karayaka, H. Bora

AU - Yan, Yanjun

AU - Tay, Peter

AU - Yu, Yi Hsiang

PY - 2019/4

Y1 - 2019/4

N2 - This paper presents a wave excitation force prediction methodology for a slider crank power take-off system (PTOS) based on autoregressive filters. This study shows the feasibility of half period and zero crossing prediction of the irregular ocean waves to keep phase lock condition between the generator and the wave excitation force. This is critically important for unidirectional continuous rotation of the generator to maximize energy extraction. Results from the prediction with different filter orders are analyzed to accommodate real time processing requirements. It has been shown that 10th order filter provides the good compromise of accuracy and timely processing.

AB - This paper presents a wave excitation force prediction methodology for a slider crank power take-off system (PTOS) based on autoregressive filters. This study shows the feasibility of half period and zero crossing prediction of the irregular ocean waves to keep phase lock condition between the generator and the wave excitation force. This is critically important for unidirectional continuous rotation of the generator to maximize energy extraction. Results from the prediction with different filter orders are analyzed to accommodate real time processing requirements. It has been shown that 10th order filter provides the good compromise of accuracy and timely processing.

KW - autoregressive filter

KW - prediction

KW - slider-crank

KW - wave energy converter

KW - wave excitation force

UR - http://www.scopus.com/inward/record.url?scp=85069935845&partnerID=8YFLogxK

U2 - 10.1109/GreenTech.2019.8767127

DO - 10.1109/GreenTech.2019.8767127

M3 - Conference contribution

AN - SCOPUS:85069935845

T3 - IEEE Green Technologies Conference

BT - 2019 IEEE Green Technologies Conference, GreenTech 2019

PB - IEEE Computer Society

T2 - 2019 IEEE Green Technologies Conference, GreenTech 2019

Y2 - 3 April 2019 through 6 April 2019

ER -

Wave Excitation Force Prediction Methodology Based on Autoregressive Filters for Real Time Control

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this