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首页> 外文会议>ASME Power Conference 2005: Includes Papers from the 2005 International Conference on Power Engineering(ICOPE) pt.A >SPECTRAL ANALYSIS OF PULVERIZED COAL COMBUSTION STABILITY
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SPECTRAL ANALYSIS OF PULVERIZED COAL COMBUSTION STABILITY

机译：煤粉燃烧稳定性的光谱分析

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Flame detecting and diagnosis of combustion in modern coal-fired boilers are very important to the safe and economical operating of power generation unit. So the effective real-time detection of combustion flame stability and in-time judgment are necessary. One of the important characteristics of combustion flame is a combination of flame jet fluctuation and flickering of flame radiation, which is a time random variable and reflects the combustion conditions. In this paper, after the data are acquired through the tests in our university's laboratory, the power spectrum analysis using algorithm of fast Fourier transformation (FFT) and a self-organized neural network are applied into a diagnostic system for combustion conditions. At first, a time series of radiation intensity values of the flame, which fluctuate at a mean intensity value with a certain frequency are obtained through the photoelectric sensor. And then the time signal is converted to the power spectrum signal through the processing of FFT. Under the stable and unstable combustion conditions, the spectral intensity of the low frequency component of the converted signal has distinct magnitude. According to this method, software for the power spectrum analysis and the self-organized neural networks has been developed.
机译：现代燃煤锅炉的火焰检测和燃烧诊断对发电机组的安全和经济运行非常重要。因此，有效的实时检测燃烧火焰稳定性和及时判断是必要的。燃烧火焰的重要特征之一是火焰射流波动和火焰辐射闪烁的结合，这是时间随机变量，反映了燃烧条件。本文通过在我校实验室的测试中获得数据后，将基于快速傅里叶变换（FFT）算法和自组织神经网络的功率谱分析应用于燃烧状态诊断系统。首先，通过光电传感器获得在一定强度下以平均强度值波动的火焰的辐射强度值的时间序列。然后通过FFT处理将时间信号转换为功率谱信号。在稳定和不稳定的燃烧条件下，转换信号的低频分量的频谱强度具有明显的幅度。根据这种方法，开发了用于功率谱分析和自组织神经网络的软件。

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