Maximization of energy absorption for a wave energy converter using the deep machine learning
Li, Liang and Yuan, Zhiming and Gao, Yan (2018) Maximization of energy absorption for a wave energy converter using the deep machine learning. Energy, 165 (Part A). pp. 340-349. ISSN 1873-6785 (https://doi.org/10.1016/j.energy.2018.09.093)
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Abstract
A controller is usually used to maximize the energy absorption of wave energy converter. Despite the development of various control strategies, the practical implementation of wave energy control is still difficult since the control inputs are the future wave forces. In this work, the artificial intelligence technique is adopted to tackle this problem. A multi-layer artificial neural network is developed and trained by the deep machine learning algorithm to forecast the short-term wave forces. The model predictive control strategy is used to implement real-time latching control action to a heaving point-absorber. Simulation results show that the average energy absorption is increased substantially with the controller. Since the future wave forces are predicted, the controller is applicable to a full-scale wave energy converter in practice. Further analysis indicates that the prediction error has a negative effect on the control performance, leading to the reduction of energy absorption.
ORCID iDs
Li, Liang ORCID: https://orcid.org/0000-0002-8528-3171, Yuan, Zhiming ORCID: https://orcid.org/0000-0001-9908-1813 and Gao, Yan;-
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Item type: Article ID code: 65519 Dates: DateEvent15 December 2018Published18 September 2018Published Online13 September 2018AcceptedSubjects: Technology > Hydraulic engineering. Ocean engineering Department: Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering Depositing user: Pure Administrator Date deposited: 24 Sep 2018 08:24 Last modified: 25 Oct 2024 00:25 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/65519