基于改进天牛群优化ESN的海上风机叶片腐蚀速率预测OA

With the rapid development of offshore wind power,turbine blade corrosion rate prediction is extremely important for the daily operation and maintenance of offshore turbine blades.However,most existing studies in this field consider relatively simplistic corrosion environments,making them difficult to apply directly to the continuous operation of wind turbine blades.To solve this problem,a corrosion rate prediction model for offshore wind turbine blades is established based on the echo state network(ESN)optimized by improved beetle swarm optimization.Firstly,the corrosion principles of offshore turbine blades are analyzed,and the main corrosion factors are identified and used as model inputs.Second,to overcome the limitations of the beetle swarm optimization(BSO),including low population diversity and susceptibility to local optima,BSO incorporating differential evolution is developed to optimize the parameters of the ESN Finally,the parameter optimal ESN is applied to predict the corrosion rate of offshore wind turbine blades.The results show that the RMSE and MAPE values of the model for the leading edge and tip regions of offshore wind turbine blades are 0.188,1.526%,and 0.177 9,1.311%,respectively,which are much lower than those of the comparison model.These findings indicate that the proposed model can provide decision-making support for the corrosion protection of offshore wind turbine blades.