【作者】 蔡叶菁；

【导师】 文敦伟；

【作者基本信息】 中南大学 ， 热能工程， 2003， 硕士

【摘要】 在锅炉燃烧器管理系统中，火焰检测对防止锅炉爆炸起着重要的作用，这就要求火焰检测系统提供可靠、准确的火焰信息。 本课题的主要目标是：根据煤粉燃烧特性和火焰图像特征，利用数字图像处理和计算机技术，对300MW的燃煤电站锅炉火焰检测与燃烧诊断的方法进行研究，旨在提高火焰燃烧诊断的可靠性。本研究以图像处理技术为基础，结合基于BP神经网络的锅炉炉膛火焰燃烧状态的模式识别技术来动态检测火焰的燃烧状况。 主要完成了下述研究和开发工作： 1．在借鉴国内外火焰检测技术的基础上，根据300MW的燃煤电站锅炉的特点及现场要求，对系统进行了功能分析。针对常规火检器镜面易被污染、高温老化、视角小的缺点，本系统采用带吹扫、冷却系统的传像光纤+CCD摄像机的火焰图像传感器，它具有视角大的优点，较好地克服火焰漂移问题，完成了火焰图像采集系统的硬件设计。 2．为便于计算机对锅炉燃烧状况进行识别，针对火焰图像的特点，对获取的火焰图像进行了处理，通过实验比较，选取了最有利于提取火焰燃烧特征的处理方法。 3．根据燃煤火焰燃烧的特征，针对常规火检器普遍存在相邻燃硕十学位论文基于数字图像处理的燃煤电站锅炉火焰检测与燃烧诊断方法研究烧器之间互相干扰(即“偷看”)，使得火焰检测装置误报，本系统通过选定反应本燃烧器火焰特征区域的特征量作为BP网络的输入，有效提高系统的抗干扰能力。而基于BP神经网络的模式识别技术，能动态检测火焰的燃烧状况，提高了系统的自适应性。对火焰诊断进行了算法设计，该算法具有自学习性、自适应性，解决了常规火焰检测装置的误报问题，提高了系统的可靠性。 4.设计了系统的软件结构。更多还原

【Abstract】 Flame detection plays an important role in Burner Management System for avoiding boilers exploding. In order to ensure the safety of boilers, flame signals provided by flame detection system must be reliable and precise.The main purpose of this research is to study the method of flame detecting and combustion diagnosing for 300MW coal-fired utility boilers. The research is based on digital image processing according to coal-fired utility boilers characteristics and flame image feature characteristics. The system can detect the flame more reliably, and monitor the course of burning at the real time.The research is carried on the basis of image processing and the pattern recognition using BP neural-network. This system helps dynamic checking flame combustion status.The main work of the research is as follows:1. After consulting the flame detecting technology of overseas, considering 300MW coal-fired utility boilers characteristics and the field condition, the function of the system is analyzed. Aiming at the disadvantages of former flame detector, such as the pollutant on thesurface of flame sensor, aging caused by high temperature, and little visual angle, image transmission optical fiber and CCD with blowing and cooling system are used as flame sensor in this system. This kind of sensor has larger visual angle to overcome flame drifting, and the structure of the hardware system for flame image collecting is designed for 300MW coal-fired utility boilers.2.To facilitate that computer recognizes the state of boilers, flame images captured by camera are processed on the basis of characteristics of flame images. A processing method which is optimal to extract characteristics of flame images is gotten by experiments.3.On the basis of flame characteristics, aiming at mutually jamming between adjoining burners (named as "peeking") causing wrong alarm by former flame detectors, flame feature characteristics of the native burner as the inputs of BP to enhances anti-jamming ability effectively in this system. And the pattern recognition using BP neural-network helps dynamic checking flame combustion status, ensures the system more adaptability. The flame diagnosis algorithm, which is self-learning and self-adapting, is put forward according to the characteristic of flame image, to avoid failure diagnosis of former flame detecting. The reliability of the system is higher.4. The structure of the software system for flame detecting isdesigned for 300MW coal-fired utility boilers.更多还原