基于历史运行数据与欧氏距离的660 MW对冲燃烧锅炉受热面热偏差判断方法

基于历史运行数据与欧氏距离的660 MW对冲燃烧锅炉受热面热偏差判断方法

林增文¹，曾臻荣¹，翁培煌¹，李颖¹，江文彬¹，周晟阳²，周磊³，刘涛³，李佳³，许凯^3*

1.福建华电邵武能源有限公司，福建省邵武市 354000；2.华电电力科学研究院有限公司，浙江省杭州市 310030；3.煤燃烧与低碳利用全国重点实验室(华中科技大学)，湖北省武汉市 430074

基金资助:
国家重点研发计划项目(2023YFB4102903)；工信部2021年人工智能创新任务揭榜挂帅项目(CHDKJ22-01-126)

Thermal Deviation Detection Method for Heat Transfer Surfaces in a 660 MW Opposed-Firing Boiler Based on Historical Operation Data and Euclidean Distance

LIN Zengwen¹, ZENG Zhenrong¹, WENG Peihuang¹, LI Ying¹, JIANG Wenbin¹, ZHOU Shengyang², ZHOU Lei³, LIU Tao³, LI Jia³, XU Kai^3*

1.Fujian Huadian Shaowu Energy Co., Ltd., Shaowu 354000, Fujian Province, China; 2.Huadian Electric Power Research Institute, Hangzhou 310030, Zhejiang Province, China; 3.National Key Laboratory of Coal Combustion and Low Carbon Utilization (Huazhong University of Science and Technology), Wuhan 430074, Hubei Province, China

Supported by:
National Key Research and Development Program of China (2023YFB4102903); 2021 AI Innovation Task Challenge Program of the Ministry of Industry and Information Technology ( CHDKJ22-01-126).

摘要/Abstract

摘要： 【目的】在国家“碳达峰、碳中和”战略目标下，燃煤发电机组频繁参与深度调峰，低负荷运行时，锅炉受热面容易因流量分配不均与燃烧偏斜而产生热偏差，导致水冷壁超温爆管风险增加。基于工质焓变的传统壁温监测方法存在滞后性，且难以区分设备固有温度分布与运行异常偏差。为此，基于某660 MW超超临界对冲燃烧锅炉，提出了基于历史数据与欧氏距离的热偏差判断方法。【方法】通过分析锅炉的主要受热面壁温情况，划分负荷区间，构建温度标杆库；通过提取特征温度消除整体温度波动影响，进一步建立热偏差指标以量化实时偏差，并进行了实践运用。【结果】受热面各管屏之间会产生较大的温度差异，尤其是在高温再热器中表现明显；受热面的壁温波动一部分是系统自身特性产生的固有偏差，另一部分是运行方式调整变化带来的波动偏差。【结论】所提方法可成功识别设备固有偏差与运行波动偏差，可为锅炉安全预警系统提供实时判别依据，保障深度调峰工况下运行安全。

关键词: 燃煤发电, 超超临界锅炉, 深度调峰, 壁温分析, 欧氏距离, 热偏差

Abstract: [Objectives]　Under the national strategic goals of carbon peak and carbon neutrality, coal-fired power units frequently participate in deep peak-shaving operations at low loads, during which uneven flow distribution and combustion bias in the boiler heating surfaces cause thermal deviations, thereby increasing the risk of tube bursts due to water wall overheating. Conventional wall temperature monitoring methods, based on the enthalpy change of the working fluid, exhibit hysteresis and have difficulty in differentiating between inherent temperature distribution patterns and abnormal operational deviations. To address this, a thermal deviation discrimination method based on historical data and Euclidean distance is proposed for a 660 MW ultra-supercritical opposed-firing boiler.[Methods]　Through analyzing the wall temperature of the main heating surfaces of the boiler, the operating load range is divided into intervals and a benchmark temperature database is constructed. Characteristic temperatures are extracted to eliminate the influence of overall temperature fluctuations. Furthermore, a thermal deviation index is established to quantify real-time deviations, and the method is applied in practice.[Results]　Significant temperature differences exist among the tube panels of the heating surfaces, and this is especially apparent in the high-temperature reheater. The fluctuations in wall temperature consist of two parts: one is the inherent deviation arising from the system’s own characteristics, and the other is the fluctuating deviation brought about by adjustments in the operating mode.[Conclusions]　The proposed method successfully identifies both the inherent deviations of the equipment and the operational fluctuating deviations, and it provides a real-time discrimination basis for boiler safety early-warning systems, ensuring operational safety under deep peak-shaving conditions.

Key words: coal-fired power generation, ultra-supercritical boiler, deep peak-shaving, wall temperature analysis, Euclidean distance, thermal deviation

林增文, 曾臻荣, 翁培煌, 李颖, 江文彬, 周晟阳, 周磊, 刘涛, 李佳, 许凯. 基于历史运行数据与欧氏距离的660 MW对冲燃烧锅炉受热面热偏差判断方法[J]. 发电技术.

LIN Zengwen, ZENG Zhenrong, WENG Peihuang, LI Ying, JIANG Wenbin, ZHOU Shengyang, ZHOU Lei, LIU Tao, LI Jia, XU Kai. Thermal Deviation Detection Method for Heat Transfer Surfaces in a 660 MW Opposed-Firing Boiler Based on Historical Operation Data and Euclidean Distance[J]. Power Generation Technology.

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