Sensitivity Analysis on Neural Network Algorithm for Primary Superheater Spray Modeling
Sensitivity Analysis on Neural Network Algorithm for Primary Superheater Spray Modeling
复制标题
一次过热器喷雾建模神经网络算法的敏感性分析
DOI：
10.1080/01457632.2016.1195134
复制
发表时间：
2017
影响因子：
2.3
通讯作者：
M. Mailah
中科院分区：
文献类型：
--
作者：
N. A. Mazalan;Azlan A. Malek;M. Wahid;M. Mailah
摘要火电厂主汽温参数具有非线性、大惯性、大滞后等特点。成功地将主蒸汽温度控制在其设定值的±2°C范围内是燃煤电厂运营商的最终目标。两个最常见的主蒸汽温度回路是一级过热器喷水和二级过热器喷水。一次过热器喷水控制阀开度的建模方法很多，其中神经网络方法是最常用的方法之一。它仍然是不确定的神经网络算法类型，设置，层数和训练算法将给出最好的结果。因此，本文显示了最佳设置的神经网络算法的基础上的敏感性分析方法的一个隐藏层。神经网络的输入选择为发电机出力、主蒸汽流量、总喷水流量和二级过热器出口蒸汽温度，而输出选择为一级喷水流量控制阀开度。
ABSTRACT Nonlinear, large inertia with long dead time is always associated with the main steam temperature parameter in coal fired power plant. Successful control of the main steam temperature within ±2°C of its setpoint is the ultimate target for coal-fired power plant operators. Two of the most common main steam temperature circuit are primary superheater spray and secondary superheater spray. Various methods were used to model the primary superheater spray control valve opening, and the neural network remains one of the most popular choices among researchers. It remains inconclusive which neural network algorithm types, setup, number of layers, and training algorithm will give the best result. As such, the paper shows the best setup for the neural network algorithm based on sensitivity analysis methodology for one hidden layer. The inputs selected for the neural network are generator output, main steam flow, total spray flow, and secondary superheater outlet steam temperature, while the output selected is primary spray flow control valve opening.