基于最小二乘支持向量机的电站锅炉空预器热点检测系统研究

回转式空气预热器是火力发电机组重要的换热设备。燃料的不完全燃烧以及低负荷或停炉后空预器内气体流速低造成散热条件变差等原因会引起空预器的再燃烧事故。论文利用最小二乘支持向量机这种新的机器学习工具，分别用两种核函数建立针对三对不同火情的判别模型，超平面参数通过交叉检验的方式确定。实验结果表明，支持向量机具有很好的分类和泛化能力。从两种核函数的ROC曲线可看出对于本问题选用RBF核函数相对于多项式核函数有更高的判别准确率。

RESEARCH ON POWER PLANT BOILER AIR PREHEATER HOT SPOTS DETECTION SYSTEM BASED ON LS-SVM

Rotary air preheater is the important heat exchanger in power plant units. Recombustion accident can be caused by inadequacy combustion of fuel or badly heat-dispersed condition aroused by low air or gas velocity after boiler outage. In the paper, discriminant models of 3 pairs of fire status have been built based on Least Square Support Vector Machines (LS-SVM) for two kinds of kernel functions. Utilizing polynomial and RBF kernel, the hyperparameters of classifiers were tuned with cross-validation. Receiver Operating Characteristic(ROC) curve comparison shows that LS-SVM classifiers are able to learn quite well from the raw data samples. Experiment results show that SVM has good classification and generalization ability and RBF kernel function has more accurate than polynomial kernel function for this problem from the area under the ROC curve (AUC) values of two kernel functions.