Novel high performance wave energy converters with advanced control, reliability and survivability systems through machine-learning forecasting

EPSRC Funding: £528k Lancaster, £811k total, 2021–2024


Lancaster University: Professor G A Aggidis (PI), Professor C J Taylor (CI), Dr X Ma (CI)

Hull University: Professor D R Parsons (CI), Dr R M Dorrell (CI)

The Novel High Performance Wave Energy Converters (NHP-WEC) project aims to advance data-driven monitoring and control in connection to both device technology and sea state predictions for WEC arrays. The research proposed is simultaneously generic while also significantly contributing to the development of an existing concept device that has shown potential, namely the multi-axis TALOS that has been developed and tank tested at Lancaster University (LU). TALOS is a novel multi-axis point absorber-style device built as a 1/100th scale representation, with a solid outer hull containing all the moving parts (like a submarine or a PS Frog style WEC device). The internal PTO system is made up of an inertial mass with hydraulic cylinders that attach it to the hull. The mass makes up a significant proportion of the device, hence it moves around as the hull is pushed by various wave motions. The motion of the ball moves hydraulic cylinders causing them to pump hydraulic fluid through a circuit. The flow of this hydraulic fluid is used to turn a hydraulic motor, which is coupled to an electrical generator, to generate electricity i.e. an inertial mass PTO approach.

Key strengths of TALOS include: The arrangement of the rams allows for the mass ball to move in multiple directions, allowing energy to be captured from multiple degrees of freedom. The flow of hydraulic fluid will change as the ball’s motion changes, so an internal hydraulic smoothing circuit is utilised to regulate the output. The latest design has proven to be successful in wave tank testing and the PTO system yields a smooth output in response to time-varying inputs from waves. An analytical model has also been developed to combine data from the hull model and hydraulic rig, yielding a predicted power output of up to 3.2 kW. However, TALOS is at a very early stage of development and requires further research to advance its Technology Readiness Level (TRL).

The design, development, deployment and operation of WECs, such as TALOS, and their potential commercial use, requires a holistic understanding of the marine environment, including on-line monitoring to enhance control combined with prediction. Potential WEC deployment sites and energy resource from single devices and arrays must be determined. Operational conditions, including wave characteristics, must be quantified to estimate dynamic loads on WEC, constraining manufacturing and their real-time operation. In this context, SmartWave, developed by the University of Hull, with the ORE Catapult and Orsted, is a tool capable of deriving high resolution sea state conditions from satellite images using machine learning.

Key strengths of SmartWave: SmartWave is based on a novel forecasting methodology, capable of resolving sea state within offshore windfarms for sector O&M logistics. It integrates recent advances in all-weather satellite monitoring to map and study the temporal and spatial distribution of sea surface wave characteristics. However, existing limitations must be addressed to advance the TRL of WEC capabilities and hence fully exploit this new technology. For example, it has been developed to characterize significant wave height, whilst further research is essential in order to extract other sea state parameters, including wave height, direction and frequency. Nonetheless, since it is capable of global reach remotely, without the use of in situ sensors, SmartWave is uniquely placed to identify the selection of appropriate deployment sites depending on the device size and specification, for optimal production of electricity.

The NHP-WEC project brings together key aspects of WEC technology and the global deployment potential of SmartWave, allowing integration of novel methodologies across optimisation, control, condition monitoring and resource forecasting. These advances will together drive evidenced reductions in costs and hence provide confidence on the benefits of wave energy technology to developers and investors.