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Control of wave energy converters with machine-learning strategies

Control of wave energy converters with machine-learning strategies
Control of wave energy converters with machine-learning strategies

Supervisors - Prof. David Ingram University of Edinburgh

PhD Student - Enrico Anderlini, University of Edinburgh

Status - Complete

The Pelamis Wave Energy Convertor (WEC) is a long, floating, articulated structure that generates electricity from wave induced motion. Power is absorbed and converted by the Pelamis through real time control of the hydraulic restraint applied at the joints. The power absorption potential is a function of the machine size and shape but approaching that potential also requires optimising the control. Both must be optimised with respect to build and operating costs to minimise cost of energy of the system.


Current Pelamis designs operate at power levels which are a fraction of theoretical limits related to the swept volume of the structure, indicating that there is considerable headroom to increase the power absorption density for a given geometry. In-house simulation tools (PELs) recently extended to perform numerical optimisations of the control parameters applied at the joints have achieved considerable uplift in the power absorption and this has been borne out in tank tests, but challenges remain in the creation of robust controls for real world systems.

The doctoral student will use and develop existing in-house numerical simulation and optimisation tools to create and test new controls and to optimise parameters with respect to ranges of realistic sea-states and systems. In addition, combined geometry and control optimisation will be a very important component of the onward reduction in cost of energy, potentially involving the incorporation of sophisticated engineering cost models into the optimisation programme.