| 变负荷下超（超）临界机组过热器壁温预测 |
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| 引用本文： | 邓博,徐鸿,郭鹏,张乃强,倪永中. 变负荷下超（超）临界机组过热器壁温预测[J]. 中国电力, 2018, 51(3): 13-20. DOI: 10.11930/j.issn.1004-9649.201711189 |
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| 作者姓名： | 邓博 徐鸿 郭鹏 张乃强 倪永中 |
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| 作者单位： | 华北电力大学 能源动力与机械工程学院, 北京 102206 |
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| 基金项目： | 国家自然科学基金资助项目（51134016，51471069）;中央高校基本科研业务费专项资金资助项目（2014XS23）。 |
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| 摘 要： | 对超（超）临界机组过热器管壁温度的影响因素进行了分析，利用电厂现场DCS系统采集到的变负荷条件下的运行数据，与对应时刻管壁的温度实测数据进行了关联比较，确定了预测模型的输入变量。分析结果显示：一级、二级过热器出口汽温、主蒸汽温度、二次风E层风箱开度、有功功率等因素对过热器管壁温度的影响较为显著。采用BP神经网络算法，选取关联结果阈值超过0.70的14种主要因素进行升负荷、稳定负荷和降负荷3种条件下的管壁温度预测，预测结果与实测结果整体趋势保持一致，最大相对误差为1.42%，能够对过热器超温预警起到良好的指导作用。
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| 关 键 词： | 超（超）临界锅炉 过热器 管壁温度 灰色关联分析 BP神经网络 |
| 收稿时间： | 2017-11-28 |
| 修稿时间： | 2017-12-25 |
Prediction of Superheater Tube Wall Temperature in Supercritical/Ultra-Supercritical Boilers for Different Loading |
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| DENG Bo,XU Hong,GUO Peng,ZHANG Naiqiang,NI Yongzhong. Prediction of Superheater Tube Wall Temperature in Supercritical/Ultra-Supercritical Boilers for Different Loading[J]. Electric Power, 2018, 51(3): 13-20. DOI: 10.11930/j.issn.1004-9649.201711189 |
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| Authors: | DENG Bo XU Hong GUO Peng ZHANG Naiqiang NI Yongzhong |
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| Affiliation: | School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China |
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| Abstract: | In this paper, the influencing factors of the superheater wall temperature in supercritical/ultra-supercritical boilers are analyzed. By using the real-time operation data acquired from the DCS system in a power plant,the grey relational analysis on the measured temperature of the superheater tubes is conducted to determine the input variables of the prediction model. The results show that the influencing factors, such as the outlet steam temperature of both the primary and secondary superheaters, the main steam temperature,the layer E opening of secondary air throttles and the active power are vital to the tube wall temperature. Then by using the BP neural network algorithm, 14 main influencing factors with the threshold of more than 0.70 are used to predict the tube wall temperature at the scenario of up loading, steady loading and down loading,which concludes that the development trend of the prediction results is consistent with that of the measured results, and the largest relative error is about 1.42%. The prediction results can provide good guideline to avoid overheating. |
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| Keywords: | supercritical/ultral-supercritical boiler superheater tube wall temperature grey relational analysis BP neural network |
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