摘要
汽轮机的故障诊断对整个电厂的安全运行意义重大。根据热力参数建立计算模型可以及早地观测到性能退化趋势,预测设备故障类型。本文采用特征通流面积的方法建立汽轮机系统性能退化模型,模拟系统故障样本与测试样本,建立设备故障样本库。通过使用改进的KNN(K-nearest neighbor)算法,基于汽水系统热力参数变化规律,计算当前机组运行数据样本相对于设备故障样本的相似度,判定当前机组各设备已发生故障的概率。通过对某S109FA联合循环机组汽轮机研究结果表明,特征通流面积在不同工况下的计算误差均在5%以内,满足工程计算要求。相比于传统KNN算法,改进KNN算法通过样本评估近邻在决策过程中的权重,取得了比传统KNN算法更高的分类正确率。对测试样本故障诊断结果表明,改进KNN算法比传统KNN算法诊断准确率更高,对测试样本诊断准确率为100%,采用改进KNN算法汽轮机系统故障诊断具有可行性,与现场实际情况吻合。
The fault diagnosis of steam turbine is of great significance to the operation of power plant.Models based on thermal parameters can be used to observe performance degradation of equipment and to predict its failure type.This paper uses the characteristic flow area method to establish the performance degradation model of the steam turbine system,simulates the system fault samples and test samples,and establishes the equipment fault sample library.Through the improved K-nearest neighbor(KNN)algorithm,based on the change law of thermal parameters of the steam and water system,the similarity of the current unit operating data samples to the equipment failure samples is calculated,and the probability of the current unit equipment failures is determined.The research shows that,the calculation error of the characteristic flow area under different operation conditions are all within 5%,which meets the calculation requirements.Compared with the conventional KNN algorithm,the improved KNN algorithm evaluates the weight of neighbors in the decision-making process through samples,and achieves a higher classification accuracy rate.The fault diagnosis results of the test sample show that,the improved KNN algorithm has a higher diagnostic accuracy rate than the conventional KNN,and the diagnostic accuracy of the test samples is 100%.The use of the improved KNN algorithm for steam turbine system fault diagnosis is feasible and consistent with the actual situation.
作者
闾城
陈时熠
华心果
向文国
LYU Cheng;CHEN Shiyi;HUA Xinguo;XIANG Wenguo(Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education,Southeast University,Nanjing 210096,China)
出处
《热力发电》
CAS
CSCD
北大核心
2021年第7期84-90,共7页
Thermal Power Generation
基金
国家科技重大专项(2017-I-0002-0002)。
关键词
联合循环
汽轮机
特征通流面积
KNN算法
故障诊断
热力参数
combined cycle
steam turbine
characteristic flow area
KNN algorithm
fault diagnosis
thermodynamic parameters
