Abstract:

Objectives In cold regions, the icing problem on wind turbine blades significantly reduces power generation efficiency and increases safety risks, making accurate icing prediction technology crucial. To improve the accuracy of ice accretion prediction for wind turbine blades, this study proposes an ice accretion prediction model based on multi-layer perceptron neural network. Methods The study combines orthogonal experiments with computational fluid dynamics to collect ice accretion feature data for wind turbine blades under different operating conditions. Based on these data, two prediction models are developed: multiple linear regression and multi-layer perceptron neural network. Results Performance evaluations using metrics such as average relative error and maximum relative error reveal that the ice accretion prediction model based on multi-layer perceptron neural network achieves an average relative error of less than 7% and a maximum relative error of less than 20% in predicting both ice mass and maximum ice thickness for glaze ice. For rime ice, the model achieves an average relative error of less than 3% and a maximum relative error of less than 13%. By comparison, the multi-layer perceptron neural network model outperforms the multiple linear regression model in terms of relative error and other metrics. Conclusions This study provides a novel and more accurate method for ice accretion prediction in the wind power industry, contributing to improved safety and efficiency in wind power generation.

Key words: wind power generation, neural network, multi-layer perceptron, wind turbine blade icing, rime ice, glaze ice, prediction model, wind farm