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投稿时间：2018-12-10
投稿时间：2018-12-10
中文摘要: 利用MATLAB软件中自带的神经网络算法模块对经典文献所载数据和方法进行了校核。在该方法的基础上，将某330 MW机组在深度调峰期间低负荷下的运行数据作为已知数据，就地实测的选择性催化还原技术（SCR）入口NOx排放值作为输出值，采用经典的Levenberg-Marquardt训练算法，建立了神经网络训练模型。训练结果表明，输出值和目标值的拟合R值接近0.98，MATLAB软件自带的神经网络算法可以预报SCR入口NOx的排放值，实现了在深度调峰低负荷运行期间达到降低试验工作量、减少试验成本的目的。
Abstract:The data and methods of the classical literature are checked by the neural network algorithm module in Matlab.On the basis of this method,the operation data of a 330 MW unit under the low load during the depth peak adjustment is taken as the known data,the NOx emission values are measured at selective catalytic reduction(SCR) entrance,and the classical Levenberg-Marquard is used.The neural network training model is established.The results show that the R value of the output value and the target value fitting is close to 0.98.The neural network algorithm in Matlab can predict the NOx emission value at the selective catalytic reduction(SCR) entrance,and realize the purpose of reducing the test workload and the test cost during the low load operation of the depth peak regulation.
keywords: deep peak shaving boiler neural network
文章编号：20193004 中图分类号：TM621.2 文献标志码：
基金项目:国网新疆电力有限公司科技项目（大型火力发电机组深度调峰技术研究，5230DK17000R）。
| 作者 | 单位 | |
| 景雪晖 | 国网新疆电力有限公司电力科学研究院 | 63098461@qq.com |
| 张涛 | 华电新疆发电有限公司昌吉热电厂 | |
| 周曙明 | 华电新疆发电有限公司昌吉热电厂 | |
| 许雷 | 国网新疆电力有限公司电力科学研究院 |
引用文本：
景雪晖,张涛,周曙明,等.灵活性深度调峰下锅炉NOx排放的神经网络方法预报[J].上海电力大学学报,2019,35(3):215-220.
JING Xuehui,ZHANG Tao,ZHOU Shuming,et al.Neural Network Prediction of NOx Emission Characteristics of Boiler under Flexibility and Deep Peak Shaving[J].Journal of Shanghai University of Electric Power,2019,35(3):215-220.
景雪晖,张涛,周曙明,等.灵活性深度调峰下锅炉NOx排放的神经网络方法预报[J].上海电力大学学报,2019,35(3):215-220.
JING Xuehui,ZHANG Tao,ZHOU Shuming,et al.Neural Network Prediction of NOx Emission Characteristics of Boiler under Flexibility and Deep Peak Shaving[J].Journal of Shanghai University of Electric Power,2019,35(3):215-220.