Shape Optimization of Oscillating Buoy Wave Energy Converter Based on the Mean Annual Power Prediction Model

Abstract

In order to improve the energy capture efficiency of an oscillating buoy wave energy converter (WEC), a buoy-shape optimization design method based on the mean annual power prediction model is proposed. According to the statistical data of long-term wave characteristics in the Chinese sea area, the optimal design space is determined. Sixty-three sample points were randomly selected in the optimized space. Based on simulation, the mean annual power corresponding to each sample point is calculated to quantitatively describe the energy capture ability. The response surface method (RSM), radial basis function neural network (RBFNN), and elliptical basis functions neural network (EBFNN) are used to establish the mean annual power prediction models, respectively. By combining the prediction model with the multi-island genetic algorithm (MIGA), the optimal solution in the design space is easily obtained. The reliability of the optimal solution is further proved by quantitative analysis about the influence of optimization parameters on the mean annual captured power. Compared with the common RSM and RBFNN methods, the prediction model established by the EBFNN method has a higher prediction accuracy. In the optimization process, the simulation calculation is replaced by a prediction model, which can effectively solve the problem of high simulation calculation cost.