发电技术2026，Vol.47Issue(1)：65-74,10.DOI:10.12096/j.2096-4528.pgt.260106
基于多层感知器神经网络的风机叶片覆冰预测模型研究
Research on Ice Accretion Prediction Model for Wind Turbine Blades Based on Multi-Layer Perceptron Neural Network
摘要
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.关键词
风力发电/神经网络/多层感知器/风机叶片覆冰/霜冰/明冰/预测模型/风电场Key words
wind power generation/neural network/multi-layer perceptron/wind turbine blade icing/rime ice/glaze ice/prediction model/wind farm分类
能源科技引用本文复制引用
韩斌,曾志祥,孔繁新,谢楠,刘志强..基于多层感知器神经网络的风机叶片覆冰预测模型研究[J].发电技术,2026,47(1):65-74,10.基金项目
国家自然科学基金项目(52206037) （52206037）
湖南省科技创新计划项目(2023GK1050,2023JJ60154).Project Supported by National Natural Science Foundation of China(52206037) （2023GK1050,2023JJ60154）
Science and Technology Innovation Program of Hunan Province(2023GK1050,2023JJ60154). （2023GK1050,2023JJ60154）
