Development and Validation of an AI-Driven Model for the La Rance Tidal Barrage: A Generalisable Case Study

• URL: http://arxiv.org/abs/2202.05347v1
• Date: Thu, 10 Feb 2022 22:02:52 GMT
• Title: Development and Validation of an AI-Driven Model for the La Rance Tidal Barrage: A Generalisable Case Study
• Authors: T\'ulio Marcondes Moreira, Jackson Geraldo de Faria Jr, Pedro O.S. Vaz-de-Melo and Gilberto Medeiros-Ribeiro
• Abstract summary: An AI-Driven model representation of the La Rance tidal barrage was developed using novel parametrisation and Deep Reinforcement Learning techniques. Results were validated with experimental measurements, yielding the first Tidal Range Structure (TRS) model validated against a constructed tidal barrage.
• Score: 2.485182034310303
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work, an AI-Driven (autonomous) model representation of the La Rance tidal barrage was developed using novel parametrisation and Deep Reinforcement Learning (DRL) techniques. Our model results were validated with experimental measurements, yielding the first Tidal Range Structure (TRS) model validated against a constructed tidal barrage and made available to academics. In order to proper model La Rance, parametrisation methodologies were developed for simulating (i) turbines (in pumping and power generation modes), (ii) transition ramp functions (for opening and closing hydraulic structures) and (iii) equivalent lagoon wetted area. Furthermore, an updated DRL method was implemented for optimising the operation of the hydraulic structures that compose La Rance. The achieved objective of this work was to verify the capabilities of an AI-Driven TRS model to appropriately predict (i) turbine power and (ii) lagoon water level variations. In addition, the observed operational strategy and yearly energy output of our AI-Driven model appeared to be comparable with those reported for the La Rance tidal barrage. The outcomes of this work (developed methodologies and DRL implementations) are generalisable and can be applied to other TRS projects. Furthermore, this work provided insights which allow for more realistic simulation of TRS operation, enabled through our AI-Driven model.

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