基于图像卷积变分自编码的电站锅炉 燃烧稳定性评价方法

为实现基于电站锅炉火焰图像的燃烧稳定性定量表征，并克服不稳定燃烧样本不足的训练难题，提出一种基于卷积变分自编码模型的燃烧稳定性实时、定量表征方法。首先使用稳定燃烧工况下的火焰图像进行模型训练，利用卷积变分自编码器得到稳定燃烧图像的高维潜在概率分布。记录该模型对应的隐变量分布特征，计算该分布与标准正态分布之间的KL散度值，利用该KL散度实现燃烧稳定性的定量表征。在仿真验证中，通过对比说明引入变分推断理论可提高模型对于燃烧图像的重构质量，图片重构前后均方根误差为0.005 48;通过磨煤机给煤量调整实验，人为制造不同稳定度的燃烧器燃烧工况，验证了该评价方法的准确性和有效性，评价准确率高达92.1%;通过与煤火检评价结果的比较，表明该方法具备煤火检系统对于火焰的定量判断功能，且感知能力更加灵敏，能在燃烧器灭火前167 s给出燃烧不稳定的预警，具有一定的工程应用价值。

Combustion stability judgment of power plant boiler based on image convolutional variational auto-encoder

To realize the quantitative characterization of combustion stability based on boiler flame images and overcome the training problem of insufficient unstable combustion samples, a real-time and quantitative characterization method of combustion stability based on the convolutional variational autoencoding model is proposed. First, the model is trained by using the flame images under stable combustion conditions, and the high-dimensional latent probability distribution of the stable combustion image is obtained by using the convolutional variational autoencoder. The distribution characteristics of the latent variables corresponding to the model are recorded, the KL divergence value between the distribution and the standard normal distribution is calculated. The KL divergence is used to realize the quantitative characterization of combustion stability. In the simulation verification, the comparison experiments show that the introduction of variational inference theory can improve the reconstruction quality of the model for the combustion image, and the root mean square error before and after image reconstruction is 0. 005 48. The accuracy and effectiveness of the evaluation method are verified through the experiment of adjusting the coal feeding amount of the coal mill to artificially create the combustion conditions of the burner with different degrees of stability, and the evaluation accuracy rate is as high as 92. 1%. The comparison results with the coal fire inspection and evaluation show that the method has the quantitative judgment function of the coal fire inspection system for flame, and the sensing ability is more sensitive. It can give the warning of combustion instability in 167 s before the burner fires, which has certain engineering application value.