Session: 09-07-01: Floating Solar Energy I

Submission Number: 157094

Predicting Floating Photovoltaic Platform Multi-Variable Dynamic Responses With Neural Network Learning Methods 

As global regulators have put an emphasis on turning towards renewable methods of energy production, Floating Photovoltaics has emerged as a growing renewable energy source, first establishing itself with inland installations before expanding into more challenging offshore conditions. Floating Photovoltaics platforms experience significant environmental loading in offshore conditions requiring a rigorous design process and increased monitoring requirements once deployed. On-site monitoring of the FPVs can be difficult for several reasons with installation challenges, environmental conditions, difficulties in access, cost-effectiveness and performance of sensors in such harsh conditions. Alternative methods of monitoring the structure with minimal onboard instrumentation would prove beneficial. Here, Neural Network (NN) Machine Learning, when adequately trained, can be effective in predicting the dynamic responses of a FPV platform using minimal sensor input. This paper aims to demonstrate how NN can predict dynamic responses of a Froude-scaled FPV platform in a wave flume by training it on wave profile, mooring line tension and platform motion data. The study assesses the performance of a Neural network with the goal of providing insight on the applications of neural networks in aiding wave flume tests for photovoltaic platforms. The results of this study indicate that a neural network trained on a limited experimental dataset can predict the multi-variable responses of an FPV platform across a range of wave conditions, with certain conditions performing better than others. The neural network performs better in predicting larger wave amplitude conditions, with the heave and pitch predictions performing better than the surge predictions.

Presenting Author: Morgan Campbell Queens University Belfast

Presenting Author Biography: Masters in Engineering from Queens University Belfast. Currently a PhD student at Queen’s University Belfast. Areas of interest include offshore renewable energy, Floating Photovoltaics (FPVs), Semi-Submersible Platform arrays, physical experimentation, machine learning & digital twinning.