Reactive control of a wave energy converter using artificial neural networks

被引：63

作者：

Anderlini E. ^{[1
,3
,4
]}

Forehand D.I.M. ^{[2
]}

Bannon E. ^{[3
]}

Abusara M. ^{[4
]}

机构：

[1] Industrial Doctoral Centre for Offshore Renewable Energy, University of Edinburgh, Faraday Building, Colin Maclaurin Road, Edinburgh

[2] Institute of Energy Systems, University of Edinburgh, Faraday Building, Colin Maclaurin Road, Edinburgh

[3] Wave Energy Scotland, 10 Inverness Campus, Inverness

[4] College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus, Penryn

来源：

International Journal of Marine Energy | 2017年 / 19卷

基金：

英国工程与自然科学研究理事会;

关键词：

Artificial Neural Networks (ANNs); Multistart optimization; Reactive control; Wave Energy Converter (WEC);

D O I：

10.1016/j.ijome.2017.08.001

中图分类号：

学科分类号：

摘要：

A model-free algorithm is developed for the reactive control of a wave energy converter. Artificial neural networks are used to map the significant wave height, wave energy period, and the power take-off damping and stiffness coefficients to the mean absorbed power and maximum displacement. These values are computed during a time horizon spanning multiple wave cycles, with data being collected throughout the lifetime of the device so as to train the networks off-line every 20 time horizons. Initially, random values are selected for the controller coefficients to achieve sufficient exploration. Afterwards, a Multistart optimization is employed, which uses the neural networks within the cost function. The aim of the optimization is to maximise energy absorption, whilst limiting the displacement to prevent failures. Numerical simulations of a heaving point absorber are used to analyse the behaviour of the algorithm in regular and irregular waves. Once training has occurred, the algorithm presents a similar power absorption to state-of-the-art reactive control. Furthermore, not only does dispensing with the model of the point-absorber dynamics remove its associated inaccuracies, but it also enables the controller to adapt to variations in the machine response caused by ageing. © 2017 The Authors.

引用

页码：207 / 220

页数：13

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