基于模糊C均值聚类算法的锅炉燃烧诊断方法

Combustion diagnostic method for boilers based on fuzzy C-means clustering algorithm

锅炉燃烧诊断对燃烧调整具有重要意义。本文提出采用表征炉膛燃烧状况的火检信号平均值、标准差、均匀度、变异系数、峰峰值为特征量,使用模糊C均值聚类算法进行燃烧诊断,分析被选特征量应具备的属性,同时对诊断过程中样本的采集周期进行了详细的研究。结果表明:采用本文所选的特征量,其聚类中心在数值上的变化规律能准确反映实际燃烧状况;采集周期对燃烧状况诊断的正确性有较大影响,当采集周期为5 s时,各聚类中心在数值上的变化规律与实际燃烧状况的变化规律不完全吻合,其诊断正确率为80%,当采集周期为10 s和30 s时,诊断正确率为100%;当求取聚类中心时样本的采集周期和待诊断样本的采集周期不同时,有可能引发误诊断。在实际应用中,应保持样本采集周期一致。

Combustion diagnosis is very important for combustion optimization of boilers.In this study,the average value,standard deviation,uniformity,coefficient of variation,and peak-to-peak value of the flame detecting signals that characterizes the combustion state of the furnace are adopted as characteristic variables.The fuzzy C-means clustering algorithm is used for combustion diagnosis.Properties of the selected features are analyzed and the acquisition cycle of the sample during the diagnostic process is researched in detail.The results show that,the variation of the clustering center in numerical value can accurately represent the practical combustion state by using the selected features.The acquisition cycle plays an important role on accuracy of the combustion diagnosis.When the acquisition cycle is 5 seconds,there is a difference in the variation trend between the clustering center in numerical value and the practical combustion state,and the diagnostic accuracy rate is only 80%.When the acquisition cycle is 10 seconds or 30 seconds,100%accuracy of the diagnostic is obtained.It may result in misdiagnosis when the acquisition cycles of samples for calculating the clustering centers is different from the acquisition cycles of samples to be diagnosed.In practical applications,it is necessary to keep the acquisition cycle consistent.