【作者】 郝祖龙；

【导师】 刘吉臻；

【作者基本信息】 华北电力大学（北京） ， 热能工程， 2010， 博士

【摘要】 燃烧优化是目前火电厂实现节能减排的有效途径之一,及时、准确地掌握锅炉燃烧状态是实现燃烧优化控制的重要前提。现有的炉膛火焰检测装置主要用于判断火焰的有或无,无法对燃烧状态作出有效评判。本文根据燃烧状态与某些测量信号的相关性,从大量数据中筛选出燃烧状态相关信号；通过分析典型工况下这些信号的统计规律,从中提取出能反映燃烧状态变化的特征量；在此基础上分别采用模式分类、信息融合等方法对燃烧状态进行识别与诊断。论文利用厂级监控信息系统(SIS)中保存的丰富数据,围绕燃烧状态相关信号的选取、燃烧状态特征提取、燃烧状态识别以及燃烧稳定性诊断这四个方面,展开了以下研究：1、研究了燃烧状态相关信号的选取问题。提出了一种基于滑动窗方差的信号相关性分析方法,可以有效分析热工信号在燃烧特征频段上的波动相似性。在此基础上,利用小波变换提出一种热工信号多尺度相关性分析方法,能够从不同频率尺度分析信号的相关性,并将该方法用于燃烧状态相关信号的选取。通过对现场众多测点进行筛选,将火检信号、炉膛压力、主汽压力、汽包水位以及空预前氧量信号作为燃烧状态相关信号。2、研究了锅炉燃烧状态特征提取问题。分八种典型工况比较了不同燃烧状态下燃烧状态相关信号的时域特性,并运用时域分析和复杂性测度提取这些信号的特征,通过对多组样本的统计分析,最终将均值、标准差、峰峰值以及复杂度作为燃烧状态相关信号的特征量。3、利用所提取的燃烧特征量,研究了燃烧状态识别问题。通过构建自组织神经网络对不同工况下提取的燃烧特征进行聚类,可以定性分析燃烧特征量与燃烧状态之间的非线性映射关系。进而利用支持向量机建立了典型工况下燃烧状态的识别模型。采用网格搜索与交叉验证相结合的方法选取模型参数,通过实际数据计算,比较了三种类型支持向量机的分类性能,并验证了该方法的有效性。4、利用信息融合思想,将证据理论应用于燃烧稳定性诊断。针对基本可信度分配不易获取的问题,提出一种基于自组织神经网络的基本可信度分配构造方法。在此基础上,应用证据理论对燃烧状态相关信号进行融合,并根据融合后的信息对燃烧状态进行诊断。通过现场数据分析,表明该方法具有较好的诊断效果,并在准确性和可靠性方面优于火检或炉膛负压等单一信号的诊断结果。更多还原

【Abstract】 Combustion optimization is one of the effective ways to realize energy saving and emission reduction in thermal power plants. Judging combustion state quickly and accurately is an important prerequisite for combustion optimizing control. Existing flame monitoring devices are nearly used to judge whether there is flame, and therefore combusiton state cannot be evaluated. Based on correlations of combustion states and measured signals, this dissertation finds out related signals of combustion states from large amounts of measured data, picks up features which can reflect variation of combustion states via analysis of statistical rules, and then identify and diagnose combustion stability by data classification and information fusion techniques respectively. The four questions of signals selection, features extraction of combustion states, recognition and diagnosis on combustion stability are studied through analysis of data stored in Supervisory Information System in Plant Level (SIS), and the main work of this dissertation can be presented as following:1. Selection of signals correlated with combustion states. Firstly, a new correlation analysis method was proposed based on calculating the variance of data in sliding window, which could effectively evaluate signal fluctuation similarity of combustion characteristic frequency range. Then, a multi-scale correlation analysis of thermal signal based on wavelet transform was proposed to study their correlation in different frequency ranges. Finally, the proposed method was applied in a 600 MW thermal unit, and flame monitoring signals, furnace pressure, main steam pressure, drum water level and oxygen content before air pre-heater were chosen as related signals.2. Feature extraction of combustion states. Characteristics in time-domain of selected signals under eight typical conditions were compared. Signal features were extracted via statistics and complexity measures, and mean values, standard deviations, peak-to-peak values and complexity were selected as features of related signals.3. Combustion states pattern recognition was studied by extracted features. Firstly, self-organizing neural network was adopted to cluster combustion features of different working conditions, and the result indicates that the neural network could analyze the nonlinear mapping relation between combustion state and their features. Then, the square support vector machines were used to recognize combustion states. The SVMs model parameters were chosed by grid-searching combined with cross-validating. Finally, the classication accuracy of three types of SVMs was compared and the effectiveness of the method was verified through on-site data calculation.4. According to information fusion theory, combustion stability diagnosis was studied based on evidence theory. With regard to the question of how to obtain basic probability assignment (BPA), a new method based on self-organizing neural network was used to obtain BPA values. Then, related signals were fused via evidence theory, and fused information was adopted to diagnose combustion states. The data analysis results showed that the proposed method was effective for combustion stability diagnosis, and its accuracy and reliability were better than that of judging by a single sensor such as flame monitoring signals or furnace pressure.更多还原