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    The impact of a high-fidelity wave-to-wire model in control parameter optimisation and power production assessment


    Penalba, Markel and Ringwood, John (2018) The impact of a high-fidelity wave-to-wire model in control parameter optimisation and power production assessment. In: ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. ASME. ISBN 9780791851319

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    Abstract

    Accurate control parameter optimisation and power production assessment is essential to evaluate the performance of a wave energy converter (WEC). However, commonly used numerical models excessively simplify the power take-off (PTO) system of the WEC, which may strongly affect power production predictions. Therefore, the present paper compares a commonly used WEC model that includes nonlinear viscous losses and an ideal PTO system model, referred to as NLideal, with a highfidelity wave-to-wire model (HFW2W) model. Results show the incapacity of the commonly used NLideal model to accurately optimise control parameters, particularly using reactive control. Likewise, the annual mean power production (AMPP) predicted using the NLideal model is significantly overestimated, with differences of up to 160% with respect to the more realistic HFW2W model. More dramatically, the use of control parameters optimised with the NLideal model in the HFW2W model results in negative AMPP, meaning that the WEC consumes more energy than it produces.

    Item Type: Book Section
    Additional Information: Cite as: Penalba, Markel, and Ringwood, John V. "The Impact of a High-Fidelity Wave-to-Wire Model in Control Parameter Optimisation and Power Production Assessment." Proceedings of the ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. Volume 10: Ocean Renewable Energy. Madrid, Spain. June 17–22, 2018. V010T09A039. ASME. https://doi.org/10.1115/OMAE2018-77501
    Keywords: high-fidelity wave-to-wire model; control parameter; optimisation; power production assessment; Ocean Renewable Energy; energy generation; eomputer simulation, wave energy converters;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 13346
    Identification Number: https://doi.org/10.1115/OMAE2018-77501
    Depositing User: Professor John Ringwood
    Date Deposited: 01 Oct 2020 16:45
    Publisher: ASME
    Refereed: Yes
    URI:

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