A Pitch Wave Force Prediction Algorithm for the Inertial Sea Wave Energy Converter

In this paper we introduce a pitch wave force predictor for a wave energy converter. The designed predictor is composed of two subsystems. First, we design a virtual sensor in order to provide a real-time estimate of the pitch wave force acting on the converter, from measurements provided by a set of on-board sensors. Then, we build an autoregressive predictor on such a virtual measurement to predict the future value of the wave force over a finite prediction horizon length. Simulation results, performed by applying the proposed algorithm on a first-principles based Simulink model of the considered sea wave energy converter, demonstrate the effectiveness of the proposed approach.

[1]  M. Pastorelli,et al.  ISWEC: Design of a prototype model with gyroscope , 2009, 2009 International Conference on Clean Electrical Power.

[2]  Xiaoguo Zhou,et al.  Study on Wave Impact Force Prediction of Different Shore Connecting Structure Based on Improved BP Neural Network , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[3]  Giuliana Mattiazzo,et al.  Experimental validation of the ISWEC wave to PTO model , 2016 .

[4]  Patrick van der Smagt Minimisation methods for training feedforward neural networks , 1994, Neural Networks.

[5]  Michael O'Neill,et al.  Neural Networks for Supervised Learning , 2015 .

[6]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[7]  John V. Ringwood,et al.  Estimation and Forecasting of Excitation Force for Arrays of Wave Energy Devices , 2018, IEEE Transactions on Sustainable Energy.

[8]  W. Marsden I and J , 2012 .

[9]  Giacomo Vissio,et al.  ISWEC toward the sea - Development, Optimization and Testing of the Device Control Architecture , 2017 .

[10]  S. H. Mousavizadegan,et al.  The fast Fourier transform applied to estimate wave energy spectral density in random sea state , 2011 .

[11]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[12]  B G Cahill,et al.  Wavelet analysis applied to the wave energy resource at an irish west coast site , 2010, OCEANS 2010 MTS/IEEE SEATTLE.

[13]  G. S. Dwarakish,et al.  Wave Prediction Using Neural Networks at New Mangalore Port along West Coast of India , 2015 .

[14]  Johannes Falnes,et al.  A REVIEW OF WAVE-ENERGY EXTRACTION , 2007 .

[15]  Francesco Fusco,et al.  Short-Term Wave Forecasting for Real-Time Control of Wave Energy Converters , 2010, IEEE Transactions on Sustainable Energy.

[16]  John Ringwood,et al.  Excitation force estimation and forecasting for wave energy applications , 2017 .

[17]  V. Panchang,et al.  One-Day Wave Forecasts Based on Artificial Neural Networks , 2006 .

[18]  T Moan,et al.  Wave Prediction and Robust Control of Heaving Wave Energy Devices for Irregular Waves , 2011, IEEE Transactions on Energy Conversion.

[19]  John Ringwood,et al.  A Model for the Sensitivity ofNon-Causal Control of Wave Energy Converters toWave Excitation Force Prediction Errors , 2011 .

[20]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[21]  David G. Dorrell,et al.  An application of the Fast Fourier Transform to the short-term prediction of sea wave behaviour , 2011 .

[22]  Philip E. Gill,et al.  Practical optimization , 1981 .

[23]  Giovanni Bracco,et al.  Iswec: A Gyroscopic Wave Energy Converter , 2012 .