基于自适应EKF滤波算法的汽包水位估计方法

Boiler Water Level Estimation Method Based on Adaptive EKF Filtering Algorithm

锅炉是火力发电机组的核心设备,汽包水位是亚临界锅炉安全运行的关键参数之一。汽包水位的测量往往存在着虚假水位、传感器误差、系统微观模型建立不准等因素的影响,极易测量不准确。针对目前存在的问题,提出了一种基于自适应扩卡尔曼滤波的汽包水位估计方法。通过把系统线性化过程中所省略的那些高阶项部分全部归并到状态噪声中,该方法能够有效消除观测量中可能存在的野值和系统时变噪声所引起的传统卡尔曼滤波发散及精度不高的问题,具有显著的容错能力和鲁棒性,且能够实时跟踪锅炉汽包水位的变化,在易受扰动过程控制系统中发挥重要作用。

Boiler is the core equipment of thermal power plants. The drum water level is one of the main parameters in the safe operation of the boilers. In the measurement of the boiler water level, there usually exist some difficult influence factors such as false water level measurement, errors of sensors, incorrect micro-models of system and so on. Those factors make the system vulnerable to the effects of disturbance. In order to solve this problem, a kind of boiler water level estimation method based on expanded adaptive Kalman filter （AEKF） is put forward in this paper. Simulation results show that the proposed method can eliminate the outliers in the measurement, solve the problem of divergence and low precision in the traditional Kalman filter brought by the time-varying noise. The new method has the fault-tolerant capability to track the change in the boiler water level, which is bounded to play an important role in the process control system vulnerable to disturbances.