孙栋钦,汤占军,李英娜,陆鹏.基于ISOA−KELM的风机叶片腐蚀速率预测[J].表面技术,2022,51(11):271-278, 304.
SUN Dong-qin,TANG Zhan-jun,LI Ying-na,LU Peng.Prediction of the Corrosion Rate of Wind Turbine Blade Based on ISOA-KELM[J].Surface Technology,2022,51(11):271-278, 304
基于ISOA−KELM的风机叶片腐蚀速率预测
Prediction of the Corrosion Rate of Wind Turbine Blade Based on ISOA-KELM
  
DOI:10.16490/j.cnki.issn.1001-3660.2022.11.025
中文关键词:  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
英文关键词:seagull optimization algorithm  nuclear extreme learning machine  wind turbine blade  surface corrosion  corrosion rate prediction
基金项目:国家自然科学基金(61962031)
作者单位
孙栋钦 昆明理工大学 信息工程与自动化学院,昆明 650000 
汤占军 昆明理工大学 信息工程与自动化学院,昆明 650000 
李英娜 昆明理工大学 信息工程与自动化学院,昆明 650000 
陆鹏 云南龙源风力发电有限公司,云南 曲靖 655000 
AuthorInstitution
SUN Dong-qin Kunming University of Science and Technology, College of Information Engineering and Automation Kunming 650000 
TANG Zhan-jun Kunming University of Science and Technology, College of Information Engineering and Automation Kunming 650000 
LI Ying-na Kunming University of Science and Technology, College of Information Engineering and Automation Kunming 650000 
LU Peng Yunnan Longyuan Wind Power Generation Limited Company Yunnan Qujing, 655000 
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中文摘要:
      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。
英文摘要:
      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, the parameters of KELM were optimized using the basic seagull optimization algorithm (SOA), particle swarm optimization (PSO), and genetic algorithm (GA) to compare the prediction errors with SOA-KELM, PSO-KELM, and GA-KELM, respectively. The obtained data are divided into training and test sets in a ratio of 3:1, and the model is trained using the training set. The results show that optimizing KELM using ISOA improves the prediction accuracy of KELM, and the obtained values of Mean Absolute Error (MAE) of 0.457, Mean Square Error (MSE) of 0.280, and R-square of 0.959 are better than the above three comparison models. After a series of experiments, the R-square of ISOA-KELM model is higher than 0.95, which further proves that the model has good accuracy and robustness in predicting the corrosion rate of wind turbine blades. And the prediction accuracy of ISOA-KELM model is higher than the average when the weekly corrosion area is 1.5~3.2 cm2, and the corrosion rate in general is within this range, which shows that the prediction model can have good performance under normal circumstances. After obtaining the prediction model, the prediction experiment was conducted for the corrosion rate in January and February 2021. Calculate the average values of maximum temperature, temperature, wind speed, humidity, total precipitation, and light intensity in January and February of the past three years, and input the obtained average values and service time into the model to predict the corrosion rate of wind turbine blades in January and February of 2021. After 20 experiments, the average value of each index is obtained as MSE is 0.502, MAE is 0.531, R-square is 0.912. Because the influence of corrosion is the average of the past three years, so the prediction effect is not as good as the prediction effect of the model comparison for the test set, but still has high accuracy. It is proved that the model has good robustness and can provide decision suggestions for the maintenance plan of wind farms, so as to guarantee the safety of wind turbine blades.
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