Wave Excitation Force Estimation and Forecasting for WEC Power Conversion Maximisation

Shi, Shuo; Abdelrahman, Mustafa; Patton, Ron J.

doi:10.1109/aim.2019.8868870

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…The gray-box modeling methods mentioned above are all parameterized. Recently, a nonparametric gray-box model has been put forward in some studies, and encouraging results have been obtained [28][29][30]. The model still follows the framework of Newton's law, and the force element, which is difficult to determine, is directly replaced by a machine learning model of related variables.…”

Section: Introductionmentioning

confidence: 99%

“…The forces and moments in [26] are obtained by a PMM test and then trained as outputs for an SVM model related to speed and water depth. In the study of WEC [30], an observer-based unknown input estimator is proposed to identify the wave excitation force, then a Gaussian Process (GP) model is used to predict the wave excitation force. On the one hand, the nonparametric gray-box model directly substitutes the information of the object itself.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Model Identification of Ships and Wave Energy Converters Based on Semi-Conjugate Linear Regression and Noisy Input Gaussian Process

Liu

Xue

Huang

et al. 2021

JMSE

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Reducing the carbon emissions of ships and increasing the utilization of marine renewable energy are the important ways to achieve the goal of carbon neutrality in ocean engineering. Establishing an accurate mathematical model is the foundation of simulating the motion of marine vehicles and structures, and it is the basis of operation energy efficiency optimization and prediction of power generation. System identification from observed input–output data is a practical and powerful method. However, for modeling objects with different characteristics and known information, a single modeling framework can hardly meet the requirements of model establishment. Moreover, there are some challenges in system identification, such as parameter drift and overfitting. In this work, three robust methods are proposed for generating ocean hydrodynamic models based on Bayesian regression. Two Bayesian techniques, semi-conjugate linear regression and noisy input Gaussian process regression are used for parametric and nonparametric gray-box modeling and black-box modeling. The experimental free-running tests of the KRISO very large crude oil carrier (KVLCC2) ship model and a multi-freedom wave energy converter (WEC) are used to validate the proposed Bayesian models. The results demonstrate that the proposed schemes for system identification of the ship and WEC have good generalization ability and robustness. Finally, the developed modeling methods are evaluated considering the aspects required conditions, operating characteristics, and prediction accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Model Identification of Ships and Wave Energy Converters Based on Semi-Conjugate Linear Regression and Noisy Input Gaussian Process

Liu

Xue

Huang

et al. 2021

JMSE

View full text Add to dashboard Cite

show abstract

“…The forces and moments in [25] are obtained by a PMM test and then trained as outputs for an SVM model related to speed and water depth. In the study of WEC [26], an observer -based unknown input estimator is used to estimate the wave excitation force, then a Gaussian Process (GP) is adopted to forecast the wave excitation force.…”

Section: Introductionmentioning

confidence: 99%

Gray- and Black-Box Modeling of Ships and Wave Energy Converters Based on Bayesian Regression

Liu¹,

Xue²,

Huang³

et al. 2020

Preprint

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Establishing an accurate mathematical model is the foundation of simulating the motion of marine vehicles and structures, and it is the basis of modeling-based control design. System identification from observed input-output data is a practical and powerful method. However, for modeling objects with different characteristics and known information, a single modeling framework can hardly meet the requirements of model establishment. Moreover, there are some challenges in system identification, such as parameter drift and overfitting. In this work, three robust methods are proposed for generating ocean hydrodynamic models based on Bayesian regression. Two Bayesian techniques, semi-conjugate linear regression and noisy input Gaussian process regression, are used for parametric and nonparametric gray-box modeling and black-box modeling. The experimental free-running tests of the KVLCC2 ship model and a multi-freedom wave energy converter (WEC) are used to validate the proposed Bayesian models. The results demonstrate that the proposed schemes for system identification of the ship and WEC have good generalization ability and robustness. Finally, the developed modeling methods are evaluated considering the aspects required conditions, operating characteristics and prediction accuracy.

show abstract

Robust Data-driven Estimation of Wave Excitation Force for Wave Energy Converters

2020

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Wave Excitation Force Estimation and Forecasting for WEC Power Conversion Maximisation

Cited by 4 publications

References 19 publications

Dynamic Model Identification of Ships and Wave Energy Converters Based on Semi-Conjugate Linear Regression and Noisy Input Gaussian Process

Dynamic Model Identification of Ships and Wave Energy Converters Based on Semi-Conjugate Linear Regression and Noisy Input Gaussian Process

Gray- and Black-Box Modeling of Ships and Wave Energy Converters Based on Bayesian Regression

Robust Data-driven Estimation of Wave Excitation Force for Wave Energy Converters

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