Control Systems for Wave Energy Converters

A project concerning the control of Wave Energy Converters (WECs) based on Dielectric Elastomer Generators (DEGs). DEGs are a class of Power Take-Off (PTO) systems made of polymeric rubber-like materials, which exploit a variable-capacitance electrostatic principle to directly convert wave energy into electrical energy.

The WEACS Project involved research, design and development of a control system that includes high-level supervisory and diagnostic functions, along with low-level, real-time dynamic-control processes. WEACS is anticipated to serve as the central nervous system for a wave energy conversion device and would be connected to all systems within the device.

The WEQUAD FRAME (Wave Energy converters linear QUADratic control FRAMEwork) project applied the linear quadratic modern control synthesis principles to multiple WEC technologies, in order to build a common control development framework for the PTOs regulating control laws.

Project ForeWave aimed at developing a set of methodologies to provide high quality inputs for several types of control methodology. These methodologies are based on the device motions and states alone. Providing better methodologies to generate the inputs required by all type of control methodology will benefit the WEC industry as a whole.

This project demonstrates the feasibility of using nonlinear optimal control methods that have been gaining maturity in other industries (automotive, wind) for wave energy devices. Consideration has been given to how nonlinear control can be extended to the broader range of control challenges, such as optimising fatigue, utilising local energy storage, incorporating incoming wave and sea-state estimates to develop useful control concepts.

An Open Data Architecture (ODA) is a key enabling technology. The adoption of a WEC Open Data Architecture (ODA) will facilitate the development of common, reusable data analytics tools to provide functions such as Diagnostic Processes, Alarm Systems, Decision Support and Operator Situation Awareness tools.

Reinforcement Learning (RL) is a leading machine learning method that can learn the probabilistic relationship between chosen actions and the device behaviour or 'state'. Desirable states can be assigned 'rewards'. Undesirable states can be penalised with negative rewards. RL calculates the best long-term control strategy by summing the probabilities of future rewards. RL is robust to sensor errors, delay and drift, as well as unidentified non-linear response.

The project aim was to develop a reliable and efficient control strategy to improve the wave energy converter (WEC) conversion efficiency and survivability over a wide range of sea states. This was to be achieved by integrating some enabling technologies in control and wave prediction into a hierarchical control framework, so that it can be equipped with several important features: maximum energy output, robustness to modelling uncertainties, and survivability at different sea sates.

The Integrated Marine Point Absorber Control Tool (IMPACT) project developed software tools to enhance and accelerate the design and implementation of advanced control systems for point absorber type wave energy converters (WECs).

The control system consists of a 3-layer global solution, each one having its clearly defined goal. The control methodology as developed here is generic and applicable to any WEC type. It is meant to set a new approach in terms of WEC control design, encapsulating WEC technology specificities and placing it in a broader framework.

This project investigated the feasibility of applying an adaptive control methodology to the Mocean Wave Energy Converter (WEC) in conjunction with a fully electrical rotary Power Take Off (PTO) system.

The project investigates the feasibility of applying an adaptive control methodology to the WaveSub Wave Energy Converter (WEC) in conjunction with the Romax electro-mechanical Power Take Off (PTO) system.

The SURF-MATIC project targets development of a control system that is focused on device survival and achieving reduced loading and harshness in Wave Energy Conversion technology.