A novel control method to maximize the energy-harvesting capability of an adjustable slope angle wave energy converter

Abstract

. (2016) A novel control method to maximize the energy-harvesting capability of an adjustable slope angle wave energy converter. Renewable Energy, 97. pp. 518-531. Permanent WRAP URL:http://wrap.warwick.ac.uk/87072 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription. This paper introduces a novel control approach to maximizing the output energy of an 12 adjustable slope angle wave energy converter (ASAWEC) with oil-hydraulic power take-off. 13Different from typical floating-buoy WECs, the ASAWEC is capable of capturing wave energy 14 from both heave and surge modes of wave motions. For different waves, online determination of 15 the titling angle plays a significant role in optimizing the overall efficiency of the ASAWEC. To 16 enhance this task, the proposed method was developed based on a learning vector quantitative 17 neural network (LVQNN) algorithm. First, the LVQNN-based supervisor controller detects wave 18 conditions and directly produces the optimal titling angles. Second, a so-called efficiency 19 optimization mechanism (EOM) with a secondary controller was designed to regulate 20 automatically the ASAWEC slope angle to the desired value sent from the supervisor controller. 21A prototype of the ASAWEC was fabricated and a series of simulations and experiments was 22 performed to train the supervisor controller and validate the effectiveness of the proposed control 23 approach with regular waves. The results indicated that the system could reach the optimal angle 24 within 2s and subsequently, the output energy could be maximized. Compared to the performance 25 of a system with a vertically fixed slope angle, an increase of 5% in the overall efficiency was 26 achieved. In addition, simulations of the controlled system were performed with irregular waves 27 to confirm the applicability of the proposed approach in practice. 28 29

Experimental study on hydrodynamic performance of a wave energy converter within multi-heaving-buoys

“…Moreover, an oil hydraulic PTO was directly driven by a heaving buoy under regular waves in the experiments, which verified the proposed mathematical model [22]. Compared with a WEC with a vertical fixed strut, an adjustable slope angle heaving-buoy WEC was tested in regular waves and found to provide 5% increase in overall efficiency [23].…”

Towards Real-Time Reinforcement Learning Control of a Wave Energy Converter

“…On the one hand, some studies have proposed the use of neural networks to find the optimal parameters for impedance-matching control on a time-averaged basis [21], thus being readily applicable to the centralised control of multiple WECs [22]. On the other hand, other works have focused on real-time control [19,20,23], exploiting the capability of neural networks to handle the predicted wave elevation over a future time horizon, similar to MPC. The main advantage of machine learning models for the system identification of WECs is that the same method can be used for different WEC technologies and is potentially adaptive to changes in the system dynamics, e.g., to subsystem failures or biofouling.…”

Numerical simulation and structural optimization based on an elliptical and cylindrical raft wave energy conversion device

“…In 2015, on the west side of Sicily, Iuppa et al 21 evaluated the performance of several wave energy conversion devices and showed that only by resizing the devices based on different wave climates can a large capacity factor be obtained. In 2016, Tri et al 22 used a learning vector quantitative neural network algorithm and introduced a novel control approach to maximize the output energy of an adjustable slope angle wave energy converter with an oil-hydraulic power take-off and performed simulations of the control system with irregular waves to confirm the applicability of the approach in practice. In 2017, Zheng et al 23 of Tsinghua University used the linear potential flow theory and the theory of wave diffraction and radiation to study the hydrodynamic characteristics of a raft-type, wave-powered desalination device through numerical simulations and theoretical analyses and discussed the influence of several parameters on the power capture efficiency of the wave energy.…”