Experimental Research and Improved Neural Network Optimization Based on the Ocean Thermal Energy Conversion Experimental Platform

Abstract

With the progress of research on ocean thermal energy conversion, the stabI have checked and revised all. le operation of ocean thermal energy conversion experiments has become a problem that cannot be ignored. The control foundation for stable operation is the accurate prediction of operational performance. In order to achieve accurate prediction and optimization of the performance of the ocean thermal energy conversion experimental platform, this article analyzes the experimental parameters of the turbine based on the basic experimental data obtained from the 50 kW OTEC experimental platform. Through the selection and training of experimental data, a GA-BP-OTE (GBO) model that can automatically select the number of hidden layer nodes was established using seven input parameters. Bayesian optimization was used to complete the optimization of hyperparameters, greatly reducing the training time of the surrogate model. Analyzing the prediction results of the GBO model, it is concluded that the GBO model has better prediction accuracy and has a very low prediction error in the prediction of small temperature changes in ocean thermal energy, proving the progressiveness of the model proposed in this article. The dual-objective optimization problem of turbine grid-connected power and isentropic efficiency is solved. The results show that the change in isentropic efficiency of the permeable device is affected by the combined influence of the seven parameters selected in this study, with the mass flow rate of the working fluid having the greatest impact. The MAPE of the GBO model turbine grid-connected power is 0.24547%, the MAPE of the turbine isentropic efficiency is 0.04%, and the MAPE of the turbine speed is 0.33%. The Pareto-optimal solution for the turbine grid-connected power is 40.1792 kW, with an isentropic efficiency of 0.837439.