摘要
目的针对风机运行安全问题,建立风机叶片表面腐蚀速率预测模型,实现对风机叶片安全的预警。方法对风机叶片腐蚀的原理进行分析,探讨复合材料的腐蚀机理,根据现场实测的数据对叶片表面腐蚀速率进行预测。针对海鸥算法(SOA)易陷入局部最优的问题提出了相应的改进方案,采用logistics混沌映射取代了随机选取海鸥初始位置的方式,提高海鸥初始位置的质量;在海鸥位置更新方式中引入了Levy飞行策略,使得海鸥算法有更强的全局搜索能力;采用Metropolis准则,使处于较差位置的海鸥个体也有一定概率被接受,以提高种群多样性。将改进的海鸥算法用于对核极限学习机(KELM)参数的寻优,建立ISOA−KELM风机叶片表面腐蚀速率预测模型。对该模型进行实验,并与SOA−KELM、PSO−KELM、GA−KELM进行预测误差对比。结果使用ISOA优化KELM提升了KELM的预测精度,获得的平均绝对误差(MAE)为0.457、均方误差(MSE)为0.280、确定系数(R−square)为0.959,均优于SOA−KELM、PSO−KELM、GA−KELM对比模型。结论用ISOA−KLEM模型建立的风机叶片表面腐蚀速率模型具有更高的预测精度,基于相关环境数据预测的腐蚀速率对风电场的维修计划具有良好的指导作用。
To scientifically stimulate the wind turbine blades maintenance plan and to protect the safety of wind farm personnel and property,the corrosion mechanism analysis of raw material for wind turbine blades was conducted.It was found that there are five main factors affecting the corrosion rate,which are temperature,external load,humidity,light,and the aging time of the material itself.Therefore,for the wind turbine blade in service,the influencing factors considered in this study are maximum temperature,average temperature,wind speed,humidity,precipitation,light intensity,and blade service time.Weekly maximum temperature,average temperature,average wind speed,average humidity,total precipitation,average light intensity,and service time of the wind turbine blades are obtained from the wind farm database and weather stations.These data are used to train the model to predict the corrosion rate of the wind turbine blades.The prediction model consists of a classifier and an optimization algorithm.A Kernel Extreme Learning Machine(KELM)was chosen as the classifier,and the hyper parameters of the KELM are optimized using an optimization algorithm to improve the classification performance.The corresponding improvement scheme is proposed to solve the problem that the SOA is easy to fall into local optimal.The method of randomly selecting the initial position of the seagull is replaced by the method of logistics chaotic mapping to improve the quality of the initial position of the seagull.The Levy flight strategy is introduced in the update method of seagull position,which makes the Seagull Optimization Algorithm have stronger global search ability.Metropolis criterion is adopted to make seagull individuals in poor positions have a certain probability to be accepted and improve the diversity of the population.The modified SOA is used to optimize the parameters of KELM,and establishes prediction model of corrosion rate on the surface of ISOA-KELM wind turbine blades.To verify the prediction performance of the ISOA-KELM model,
作者
孙栋钦
汤占军
李英娜
陆鹏
SUN Dong-qin;TANG Zhan-jun;LI Ying-na;LU Peng(Kunming University of Science and Technology,College of Information Engineering and Automation Kunming 650000;Yunnan Longyuan Wind Power Generation Limited Company Yunnan Qujing,655000)
出处
《表面技术》
EI
CAS
CSCD
北大核心
2022年第11期271-278,304,共9页
Surface Technology
基金
国家自然科学基金(61962031)。
关键词
海鸥优化算法
核极限学习机
风机叶片
表面腐蚀
腐蚀速率预测
seagull optimization algorithm
nuclear extreme learning machine
wind turbine blade
surface corrosion
corrosion rate prediction
