Prediction and optimization of wave energy converter arrays using a machine learning approach
Optimization of the layouts of arrays of wave energy converters (WECs) is a challenging problem. The hydrodynamic analysis and performance estimation of such systems are performed using semi-analytical and numerical models such as the boundary element method. However, the analysis of an array of such converters becomes computationally expensive, and the computational time increases rapidly with the number of devices in the system. As such determination of optimal layouts of WECs in arrays becomes extremely difficult. In this paper, a methodology involving multiple optimization strategies is presented to arrive at the solution to the complex problem. The approach includes a statistical emulator to predict the performance of the WECs in arrays, followed by an innovative active learning strategy to simultaneously explore and focus in regions of interest of the problem, and finally a genetic algorithm to obtain the optimal layouts of WECs. The method is extremely fast and easily scalable to arrays of any size. Case studies are performed on a wavefarm comprising of 40 WECs subject to arbitrary bathymetry and space constraints.