Estimation and prediction of wave input and system states based on local hydropressure and machinery response measurements

Abstract: Waves represent a big untapped energy source and many are endeavouring to develop Wave Energy Converters (WEC) to harvest this resource. The goal of this thesis, carried out with the young technology company CorPower Ocean AB, is to enable a better control of the company’s WEC by providing control strategies with a prediction of the input force on the device, also called excitation force. Previous work is already available for wave prediction, but this time the time-series we need to predict is not available and we used instead some pressure, force and position measurements to determine the value of the force in the future. The time-series of the measurements are linked to the values of the force thanks to linear (airy) wave theory and some linearisation of the existent model for the forces applied on the WECs. Three methods were suggested: prediction using an AR model then transformation thanks to transfer functions, Kalman filtering and Wiener filtering. The two latter have a better, more or less equivalent performance in terms of Mean Square Error, but the focus was made on the Wiener filter since it didn’t require identification under the assumption of a JONSWAP spectrum for the waves. This last method was implemented in the Simulink model of CorPowerOcean and extensively tested to quantify the influence of a variation in the sea state, noise conditions or parameters of the filter. The prediction were however rarely above 60% for a half wave period in the future and use of the method as is for non-causal control is questionable. We conclude by giving some other potential solutions for pursuing work in this domain.