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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.
- artificial intelligence
- artificial neural network
- machine learning
- model predictive control
- wave energy
- wave force prediction
Wave force prediction effect on the energy absorption of a wave energy converter with real-time controlLi, L., Yuan, Z., Gao, Y. & Zhang, X., 30 Apr 2019, In: IEEE Transactions on Sustainable Energy. 10, 2, p. 615-624 10 p.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile9 Citations (Scopus)72 Downloads (Pure)
Li, L., Yuan, Z. & Gao, Y., 15 Dec 2018, In: Energy. 165, Part A, p. 340-349 10 p.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile18 Citations (Scopus)15 Downloads (Pure)