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.
| Language | English |
|---|---|
| Pages | 282-293 |
| Number of pages | 12 |
| Journal | Ocean Engineering |
| Volume | 183 |
| Early online date | 16 May 2019 |
| DOIs | |
| Publication status | Published - 1 Jul 2019 |
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Keywords
- artificial intelligence
- artificial neural network
- machine learning
- model predictive control
- wave energy
- wave force prediction
Cite this
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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 journal › Article
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 -