On the sensitivity and uncertainty of wave energy conversion with an artificial neural-network-based controller

Research output: Contribution to journalArticle

Abstract

This work addresses with sensitivity and uncertainty of the energy conversion of an oscillation-body wave energy converter with an artificial neural-network-based controller. The smart controller applies the model predictive control strategy to implement real-time latching control to the wave energy converter. Since the control inputs are future wave forces, an artificial neural network is developed and trained by the machine learning algorithm to predict the short-term wave forces based on the real-time measurement of wave elevation. The sensitivity of wave energy conversion with respect to wave frequency and receding horizon length are investigated. Uncertainties of the neural network that lead to the prediction deviation are identified and quantified, and their influences on the energy conversion are examined. The control command is derived inappropriately in the presence of prediction deviation leading to the reduction of energy absorption. Moreover, it is the phase deviation that reduces the energy absorption.

LanguageEnglish
Pages282-293
Number of pages12
JournalOcean Engineering
Volume183
Early online date16 May 2019
DOIs
Publication statusPublished - 1 Jul 2019

Fingerprint

Wave energy conversion
Neural networks
Controllers
Energy absorption
Energy conversion
Model predictive control
Real time control
Time measurement
Learning algorithms
Learning systems
Uncertainty

Keywords

  • artificial intelligence
  • artificial neural network
  • machine learning
  • model predictive control
  • wave energy
  • wave force prediction

Cite this

@article{0ad0cc91ca724d2d95098626bdd2ae7a,
title = "On the sensitivity and uncertainty of wave energy conversion with an artificial neural-network-based controller",
abstract = "This work addresses with sensitivity and uncertainty of the energy conversion of an oscillation-body wave energy converter with an artificial neural-network-based controller. The smart controller applies the model predictive control strategy to implement real-time latching control to the wave energy converter. Since the control inputs are future wave forces, an artificial neural network is developed and trained by the machine learning algorithm to predict the short-term wave forces based on the real-time measurement of wave elevation. The sensitivity of wave energy conversion with respect to wave frequency and receding horizon length are investigated. Uncertainties of the neural network that lead to the prediction deviation are identified and quantified, and their influences on the energy conversion are examined. The control command is derived inappropriately in the presence of prediction deviation leading to the reduction of energy absorption. Moreover, it is the phase deviation that reduces the energy absorption.",
keywords = "artificial intelligence, artificial neural network, machine learning, model predictive control, wave energy, wave force prediction",
author = "Liang Li and Zhen Gao and Zhi-Ming Yuan",
year = "2019",
month = "7",
day = "1",
doi = "10.1016/j.oceaneng.2019.05.003",
language = "English",
volume = "183",
pages = "282--293",
journal = "Ocean Engineering",
issn = "0029-8018",
publisher = "Elsevier",

}

On the sensitivity and uncertainty of wave energy conversion with an artificial neural-network-based controller. / Li, Liang; Gao, Zhen; Yuan, Zhi-Ming.

In: Ocean Engineering, Vol. 183, 01.07.2019, p. 282-293.

Research output: Contribution to journalArticle

TY - JOUR

T1 - On the sensitivity and uncertainty of wave energy conversion with an artificial neural-network-based controller

AU - Li, Liang

AU - Gao, Zhen

AU - Yuan, Zhi-Ming

PY - 2019/7/1

Y1 - 2019/7/1

N2 - This work addresses with sensitivity and uncertainty of the energy conversion of an oscillation-body wave energy converter with an artificial neural-network-based controller. The smart controller applies the model predictive control strategy to implement real-time latching control to the wave energy converter. Since the control inputs are future wave forces, an artificial neural network is developed and trained by the machine learning algorithm to predict the short-term wave forces based on the real-time measurement of wave elevation. The sensitivity of wave energy conversion with respect to wave frequency and receding horizon length are investigated. Uncertainties of the neural network that lead to the prediction deviation are identified and quantified, and their influences on the energy conversion are examined. The control command is derived inappropriately in the presence of prediction deviation leading to the reduction of energy absorption. Moreover, it is the phase deviation that reduces the energy absorption.

AB - This work addresses with sensitivity and uncertainty of the energy conversion of an oscillation-body wave energy converter with an artificial neural-network-based controller. The smart controller applies the model predictive control strategy to implement real-time latching control to the wave energy converter. Since the control inputs are future wave forces, an artificial neural network is developed and trained by the machine learning algorithm to predict the short-term wave forces based on the real-time measurement of wave elevation. The sensitivity of wave energy conversion with respect to wave frequency and receding horizon length are investigated. Uncertainties of the neural network that lead to the prediction deviation are identified and quantified, and their influences on the energy conversion are examined. The control command is derived inappropriately in the presence of prediction deviation leading to the reduction of energy absorption. Moreover, it is the phase deviation that reduces the energy absorption.

KW - artificial intelligence

KW - artificial neural network

KW - machine learning

KW - model predictive control

KW - wave energy

KW - wave force prediction

UR - http://www.scopus.com/inward/record.url?scp=85066149777&partnerID=8YFLogxK

UR - https://www.sciencedirect.com/journal/ocean-engineering

U2 - 10.1016/j.oceaneng.2019.05.003

DO - 10.1016/j.oceaneng.2019.05.003

M3 - Article

VL - 183

SP - 282

EP - 293

JO - Ocean Engineering

T2 - Ocean Engineering

JF - Ocean Engineering

SN - 0029-8018

ER -