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作 者:冉初萌 RAN Chumeng(Taizhou Power Generation Co.,Ltd.,CHN Energy,Taizhou 225327,China)
机构地区:[1]国家能源集团泰州发电有限公司,江苏泰州225327
出 处:《自动化仪表》2025年第3期101-105,110,共6页Process Automation Instrumentation
摘 要:电厂锅炉排放颗粒物浓度分布图像噪声含量较大,导致颗粒物浓度分布特征提取难度增加。为此,提出了基于图像特征的电厂锅炉排放颗粒物浓度分布特征提取方法。以某地燃煤电厂作为研究对象,布设电厂锅炉排放颗粒物采样点,采集电厂锅炉排放颗粒物浓度分布图像。根据小波系数方差值的估计结果确定图像去噪阈值,实现图像去噪处理。搭建颗粒物浓度分布特征提取模型,提取去噪后图像的颜色特征以及纹理特征,并通过特征整合得到颗粒物浓度分布特征。试验结果表明,该方法能够有效地检测出锅炉排放颗粒物浓度,且颗粒物浓度分布特征提取效果好。该方法对于颗粒物排放与治理领域具有重要价值。The image noise content of the concentration distribution of particulate matter emitted from power plant boilers is large,which leads to an increase in the difficulty of feature extraction of the concentration distribution of particulate matter.For this reason,a feature extraction method based on image features of the concentration distribution of particulate matter emitted from power plant boilers is proposed.A coal-fired power plant in a certain place is taken as the research object,and the sampling points of particulate matter emitted from the power plant boilers are set up,and the images of the concentration distribution of particulate matter emitted from the power plant boilers are collected.According to the estimation results of wavelet coefficient variance,the image denoising threshold is determined,and the image denoising process is realized.The particulate matter concentration distribution feature extraction model is constructed to extract the color features and texture features of the denoised image,and the particulate matter concentration distribution features are obtained through feature integration.The experimental results show that the method can effectively detect the concentration of particulate matter emitted from the boilers,and the particulate matter concentration distribution feature extraction is effective.This method is of great value in the field of particle matter emission and management.
关 键 词:图像特征 燃煤电厂 锅炉排放 颗粒物 浓度分布特征 特征提取 图像去噪
分 类 号:TH81[机械工程—仪器科学与技术]
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