Layout Optimisation of Wave Energy Converter Arrays

This paper proposes an optimisation strategy for the layout design of wave energy converter (WEC) arrays. Optimal layouts are sought so as to maximise the absorbed power given a minimum q-factor, the minimum distance between WECs, and an area of deployment. To guarantee an efficient optimisation, a four-parameter layout description is proposed. Three different optimisation algorithms are further compared in terms of performance and computational cost. These are the covariance matrix adaptation evolution strategy (CMA), a genetic algorithm (GA) and the glowworm swarm optimisation (GSO) algorithm. The results show slightly higher performances for the latter two algorithms; however, the first turns out to be significantly less computationally demanding

On building physics-based AI models for the design and SHM of mooring systems

Expert systems in industrial processes are modelled using physics-based approaches, data-driven models or hybrid approaches in which however the underlying physical models generally constitute a separate block with respect to the Artificial Intelligence (AI) technique(s). This work applies the novel concept of “imbrication”-a physics-based AI approach-to the mooring system of offshore renewable energy devices to achieve a complete integration of both perspectives. This approach can reduce the size of the training dataset and computational time while delivering algorithms with higher generalization capability and explicability. We first undertake the design of the mooring system by developing a surrogate model coupled with a Bayesian optimiser. Then, we analyse the structural health monitoring of the mooring system by designing a supervised Deep Neural Network architecture. Herein, we describe the characteristics of the imbrication process, analyse preliminary results of our investigation and provide considerations for orienting further research work and sector applicability

Numerical Simulation of Control Strategies at Mutriku Wave Power Plant

This is the author accepted manuscript. The final version is available from ASME via the DOI in this record.In order to de-risk wave energy technologies and bring confidence to the sector, it is necessary to gain experience and collect data from sea trials. As part of the OPERA H2020 project, the Mutriku Wave Power Plant (MWPP) is being used as a real condition laboratory for the experiment of innovative technologies. The plant is situated in the North shore of Spain and has been operating since 2011. It uses the Oscillating Water Column (OWC) principle, which consists in compressing and expanding the air trapped in a chamber due to the inner free-surface oscillation resulting from the incident waves. The pressure difference between the air chamber and the atmosphere is used to drive an air turbine. In that case, a self-rectifying air turbine is the best candidate for the energy conversion, as it produces a unidirectional torque in presence of a bi-directional flow. The power take-off system installed is composed of a biradial turbine connected to a 30kW off-the-shelf squirrel cage generator. One of the novelties of the turbine is a high-speed stop-valve installed close to the rotor. The valve may be used to control the flow rate through the turbine or for latching control. This paper focuses on the development, the implementation and the numerical simulation of five control strategies including turbine speed and generator torque controllers. The algorithms were designed thanks to a numerical model describing one of the OWC chambers of the Mutriku power plant. Numerical results are presented for a variety of sea states and a comparison between the proposed control laws in terms of energy production and power quality is performed.This work has been performed as part of the H2020 OPERA project GA 654444. The third author was supported by Portuguese Science Foundation, FCT researcher grant No. IF/01457/2014