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针对热工对象的时变性特点及其在运行过程中易受到不确定性干扰的影响,提出一种基于最小均方(least mean square,LMS)自适应滤波器的热工过程建模方法。LMS滤波器以未知对象的输入和输出作为激励和期望信号,通过最速下降法得到未知对象的有限脉冲响应(finity impluse response,FIR)模型,其与差分方程或传递函数是等价的。实验仿真和某电厂实际运行数据验证了该算法的有效性。这种建模方法避免了复杂的机理分析,其抽头权值的分布可以表征热工对象的动态特性,为分析热工对象提供了一种手段。
Aiming at the time-varying characteristics of thermal objects and its susceptibility to uncertainties during operation, a thermal modeling method based on least mean square (LMS) adaptive filter is proposed. The LMS filter uses the steepest descent method to obtain the finity impulse response (FIR) model of the unknown object, which is equivalent to the difference equation or transfer function, using the input and output of the unknown object as the excitation and the desired signal. Experimental simulation and actual operation data of a power plant verify the effectiveness of the algorithm. This modeling method avoids the complicated mechanism analysis. The distribution of the tap weights can characterize the dynamic characteristics of the thermal object, which provides a means for analyzing the thermal object.