卡尔曼滤波器在多元信号融合中的应用研究

发布时间：2018-03-27 10:41

本文选题：卡尔曼滤波器　切入点：煤发热量　出处：《华北电力大学》2017年硕士论文

【摘要】：随以风电为代表的可再生能源规模化并网,火电机组调峰调频任务日益艰巨。为快速响应发电负荷指令变化同时保证机组安全、经济、环保运行,需要对机组关键系统进行更加有效的监视和控制。对象关键状态参数测不到或测不准一直是困扰状态检测和优化控制的难题。事实上,如煤发热量、锅炉效率、热量信号等许多测量或软测量信号无法同时满足准确性和实时性的要求。针对同一信号,采用某种测量方法得到的结果静态误差小但动态误差大,而采用另外一种测量方法得到的结果动态误差小但静态误差大。卡尔曼滤波器在组合导航中获得成功应用。GPS定位信号静态准确度好但实时性差,惯性定位信号实时性好但静态准确度差,卡尔曼滤波器发挥两者优点,获得既准又快的综合定位信号。将这种方法借鉴到多元热工信号融合领域。以现场入炉煤发热量测量为例分析了不同测量方法的特点:实验室化验结果静态准确度高,但测量结果为离散点且存在滞后;利用锅炉效率正反平衡分析方法相结合得到的结果,机组稳定运行工况下准确度高,但变负荷工况时存在巨大的动态误差;制粉系统热力计算结合烟气成份分析得到结果也只在稳定工况下有效;而基于机组发电负荷-机前压力简化非线性动态模型构造的结果,能够较快反映煤发热量变化情况但静态基准值难以确定。不同的方法在静态准确度和实时性上各有特点,在此,研究了两种煤发热量信息融合方法:(1)结合卡尔曼滤波预估-校正实质,利用实验室化验法得到的煤发热量的数据,实时修正基于机组负荷-压力模型动态法得到的实时煤发热量值。(2)针对动态法和基于制粉系统热力计算结合烟气成份分析得到结果的静态法在实时性和静态精度上互补的特点,对其进行信息融合。利用机组实时运行数据验证,得到静态准确度和动态误差均小于4%的综合发热量信号,能够满足工程实用要求。
[Abstract]:With the large-scale grid connection of renewable energy represented by wind power, the task of peak shaving and frequency modulation of thermal power units is becoming increasingly arduous. In order to quickly respond to the change of power generation load and ensure the safety, economy and environmental protection of the units, It is necessary to monitor and control the key system of the unit more effectively. It is a difficult problem for the state detection and optimization control that the key state parameter of the object can not be measured or uncertain. In fact, the boiler efficiency, such as coal calorific value, boiler efficiency, etc. Many measuring or soft measuring signals such as heat signal can not meet the requirements of accuracy and real-time simultaneously. For the same signal, the static error is small but the dynamic error is large. However, the dynamic error is small but the static error is large by using another measuring method. The Kalman filter has been successfully used in integrated navigation. The GPS positioning signal has good static accuracy but poor real-time performance. Inertial positioning signal has good real-time performance but poor static accuracy, and Kalman filter has the advantages of both. This method is used for reference in the field of multi-element thermal signal fusion. The characteristics of different measurement methods are analyzed by taking the measurement of calorific value of coal in situ as an example: the static accuracy of laboratory test results is high. However, the measurement results are discrete points and lag, and the results obtained by combining the positive and negative balance analysis method of boiler efficiency show that the accuracy of the unit is high under stable operating conditions, but there is a huge dynamic error under variable load conditions. The results obtained from the thermodynamic calculation of pulverizing system combined with the analysis of flue gas composition are only valid under steady working conditions, but based on the results of simplified nonlinear dynamic model construction of generating load-front pressure of generating units, It can reflect the change of coal calorific value quickly, but the static reference value is difficult to determine. Different methods have their own characteristics in static accuracy and real time. This paper studies two fusion methods of coal calorific value information: 1) combined with Kalman filter prediction-correction essence, using the data of coal calorific value obtained by laboratory test method. Real-time correction is based on the real-time coal calorific value obtained by the dynamic method of unit load-pressure model. Aiming at the characteristics of the dynamic method and the static method which is based on the thermodynamic calculation of the pulverizing system combined with the smoke composition analysis, the real-time and static accuracy of the method are complementary. By using the real-time operation data of the unit, the integrated calorific signal with static accuracy and dynamic error less than 4% is obtained, which can meet the practical requirements of engineering.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM621;TN713

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