Effects of hydrodynamic interactions and control within a point absorber array on electrical output

Nambiar, Anup; Forehand, David; Kramer, Morten; Hansen, Rico Hjerm; Ingram, David

doi:10.1016/j.ijome.2014.11.002

Cited by 22 publications

(24 citation statements)

References 25 publications

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“…The same conclusion was reached by Nambiar et al (2015) after a study of three buoys of the Wavestar prototype that compared different types of resistive and reactive PTO control strategies using a dedicated time domain model including…”

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confidence: 53%

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On the numerical modeling and optimization of a bottom-referenced heave-buoy array of wave energy converters

Flavià

Babarit

Clément

2017

International Journal of Marine Energy

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To cite this version:F Fàbregas Flavià, Aurélien Babarit, Alain H. Clément. On the numerical modeling and optimization of a bottom-referenced heave-buoy array of wave energy converters. there exists an optimum number of floats for a given device footprint. Exceeding this number results in a "saturation" of the power increase, which is undesirable for the economic viability of the device. As in previous studies on multiple absorber WECs, significant differences were observed in energy production among floats, due to hydrodynamic interactions.

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confidence: 53%

“…It can be observed that the peak of the rotation along the 195 bearing axis is reduced and shifted towards lower frequencies. As mentioned in Nambiar et al (2015), the close proximity of the floats means that hydrodynamic interactions are expected to be very important.…”

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confidence: 99%

On the numerical modeling and optimization of a bottom-referenced heave-buoy array of wave energy converters

Flavià

Babarit

Clément

2017

International Journal of Marine Energy

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“…This paper neglected the interaction between the WECs in line with the wave crest, assuming these will have minor influence on the optimal damping as for example the results of [21] suggest. Further studies could focus on the influence of interaction between WECs -first on the interaction of WECs in a line parallel to the wave, then also interaction between the WECs in several rows.…”

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confidence: 99%

A Model Free Control Based on Machine Learning for Energy Converters in an Array

Thomas

Giassi

Göteman

et al. 2018

BDCC

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This paper introduces a machine learning based control strategy for energy converter arrays designed to work under realistic conditions where the optimal control parameter can not be obtained analytically. The control strategy neither relies on a mathematical model, nor does it need a priori information about the energy medium. Therefore several identical energy converters are arranged so that they are affected simultaneously by the energy medium. Each device uses a different control strategy, of which at least one has to be the machine learning approach presented in this paper. During operation all energy converters record the absorbed power and control output; the machine learning device gets the data from the converter with the highest power absorption and so learns the best performing control strategy for each situation. Consequently, the overall network has a better overall performance than each individual strategy. This concept is evaluated for wave energy converters (WECs) with numerical simulations and experiments with physical scale models in a wave tank. In the first of two numerical simulations, the learnable WEC works in an array with four WECs applying a constant damping factor. In the second simulation, two learnable WECs were learning with each other. It showed that in the first test the WEC was able to absorb as much as the best constant damping WEC, while in the second run it could absorb even slightly more. During the physical model test, the ANN showed its ability to select the better of two possible damping coefficients based on real world input data.

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Section: Conclusion Outlookmentioning

confidence: 99%

A model free control based on machine learning for energy converters in an array

Thomas¹,

Giassi²,

Göteman³

et al. 2018

Preprint

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This paper introduces a model-free, "on-the-fly" learning control strategy for arrays of energy converters with adjustable generator damping. The devices are arranged so that they are affected simultaneously by the energy medium. Each device uses a different control strategy, of which at least one has to be the machine learning approach presented in this paper. During operation all energy converters record the absorbed power and control output; the machine learning device gets the data from the converter with the highest power absorption and so learns the best performing control strategy for each state. Consequently, the overall network has a better overall performance than each individual strategy. This concept is evaluated for wave energy converter (WEC) with numerical simulations and experiments with physical scale models in a wave tank. In the first of two numerical simulations, the learnable WEC works in an array with four WECs applying a constant damping factor. In the second simulation, two learnable WECs were learning with each other. It showed that in the first test the WEC was able to absorb as much as the best constant damping WEC, while in the second run it could absorb even slightly more. During the physical model test, the ANN showed its ability to select the better of two possible damping coefficients based on real world input data.

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Effects of hydrodynamic interactions and control within a point absorber array on electrical output

Cited by 22 publications

References 25 publications

On the numerical modeling and optimization of a bottom-referenced heave-buoy array of wave energy converters

On the numerical modeling and optimization of a bottom-referenced heave-buoy array of wave energy converters

A Model Free Control Based on Machine Learning for Energy Converters in an Array

A model free control based on machine learning for energy converters in an array

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