摘要
针对温度场特征参数差异引发的锅炉温度场分割准确性的问题,以维持温度场特征为目标,引入图结构表达场数据,通过改进Node2vec算法进行聚类分析,进而实现锅炉温度场的最佳分割。该方法基于多维度的特征信息对锅炉温度场实现分割,能够更准确地保留流场特征。在标准数据集上进行了实验验证,结果表明在具有多维度特征的数据集上,所提方法相比其他对比算法在分割效果方面有提升显著。最后将提出的方法用于分割电站锅炉温度场,结果表明该方法可以很好地捕捉温度场数据中的局部和全局特征,且结果具有较好的精确性。
Aiming at the problem of segmentation accuracy of boiler temperature field caused by the difference of temperature field feature parameters,with the goal of maintaining temperature field characteristics,the graph structure is introduced to express the field data,and the clustering analysis is carried out through the improvement of Node2vec algorithm,then the optimal segmentation of the boiler temperature field is realized.Based on multi-dimensional feature information,the method can segment the boiler temperature field and preserve the flow field characteristics more accurately.The proposed method is verified by experiments on standard datasets,and the results show that the segmentation effect is significantly improved on datasets with multi-dimensional features.Finally,the proposed method is applied to segment the temperature field of a power plant boiler,and the results show that the method can capture the local and global characteristics of the temperature field data well,and the results have good accuracy.
作者
张悦
梁珊珊
ZHANG Yue;LIANG Shanshan(Department of Automation,North China Electric Power University,Baoding 071003,China;Technology Innovation Center of Simulation&Opitimized Control for Power Generation of Hebei Province,North China Electric Power University,Baoding 071003,China)
出处
《电力科学与工程》
2024年第5期72-78,共7页
Electric Power Science and Engineering
基金
河北省省级科技计划资助项目(22567643H)。
关键词
燃煤锅炉
温度场
流场分割
图结构
Node2vec
coal-fired boiler
temperature field
flow field segmentation
graph structure
Node2vec
