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数据驱动的燃煤发电装备运行工况监控——现状与展望

赵春晖 胡赟昀 郑嘉乐 陈军豪

赵春晖, 胡赟昀, 郑嘉乐, 陈军豪. 数据驱动的燃煤发电装备运行工况监控——现状与展望. 自动化学报, 2021, 47(x): 1−23 doi: 10.16383/j.aas.c200993
引用本文: 赵春晖, 胡赟昀, 郑嘉乐, 陈军豪. 数据驱动的燃煤发电装备运行工况监控——现状与展望. 自动化学报, 2021, 47(x): 1−23 doi: 10.16383/j.aas.c200993
Zhao Chun-Hui, Hu Yun-Yun, Zheng Jia-Le, Chen Jun-Hao. Data-driven operating monitoring for coal-fired power generation equipment: The state of the art and challenge. Acta Automatica Sinica, 2021, 47(x): 1−23 doi: 10.16383/j.aas.c200993
Citation: Zhao Chun-Hui, Hu Yun-Yun, Zheng Jia-Le, Chen Jun-Hao. Data-driven operating monitoring for coal-fired power generation equipment: The state of the art and challenge. Acta Automatica Sinica, 2021, 47(x): 1−23 doi: 10.16383/j.aas.c200993

数据驱动的燃煤发电装备运行工况监控——现状与展望

doi: 10.16383/j.aas.c200993
基金项目: NSFC-浙江省两化融合基金(U1709211), 工业控制技术国家重点实验室自主课题(ICT2021A15)资助
详细信息
    作者简介:

    赵春晖:浙江大学控制科学与工程学院教授. 2003年获得中国东北大学自动化专业学士学位, 2009年获得中国东北大学控制理论与控制工程专业博士学位, 先后在中国香港科技大学、美国加州大学圣塔芭芭拉分校做博士后研究工作. 主要研究方向为机器学习, 工业大数据解析与应用, 包括化工, 能源以及医疗领域. 本文通信作者. E-mail: chhzhao@zju.edu.cn

    胡赟昀:浙江大学控制科学与工程学院博士研究生. 2016年获得华北电力大学(北京)控制与计算机工程学院自动化专业学士学位. 主要研究兴趣包括多元统计分析, 过程监测和故障诊断. E-mail: huyunyun1029@126.com

    郑嘉乐:浙江大学控制科学与工程学院博士研究生. 2017年获得华北电力大学(北京)控制与计算机工程学院自动化专业学士学位. 主要研究兴趣包括时间序列分析, 过程监测与故障诊断. E-mail: Carol_Zheng@zju.edu.cn

    陈军豪:浙江大学控制科学与工程学院博士研究生. 2019年获得浙江大学机械工程学院机电工程学士学位, . 主要研究兴趣包括过程监控和模式识别. E-mail: junhaochen@zju.edu.cn

Data-Driven Operating Monitoring for Coal-Fired Power Generation Equipment: The State of The Art and Challenge

Funds: Supported by NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization (U1709211), and the Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (ICT2021A15)
More Information
    Author Bio:

    ZHAO Chun-Hui Professor at the College of Control Science and Engineering, Zhejiang University. She received her bachelor, master, and Ph. D. degrees from the Department of Automation, Northeastern University, Shenyang, China in 2003, 2006, and 2009, respectively. She was a postdoctoral fellow (January 2009-January 2012) at the Hong Kong University of Science and Technology, China and the University of California, Santa Barbara, Los Angeles, CA, USA. Her research interest covers machine learning, analytics of industrial big data, and their applications in energy and medical fields. Corresponding author of this paper

    HU Yun-Yun Ph. D. candidate at the College of Control Science and Engineering, Zhejiang University, since 2016. She received the B.Eng from School of Control and Computer Engineering, North China Electric Power University, Beijing, in 2016. Her research interests include statistical data analysis, process monitoring and fault diagnosis

    ZHENG Jia-Le Ph. D. candidate at the College of Control Science and Engineering, Zhejiang University, since 2017. She received the B.Eng from School of Control and Computer Engineering, North China Electric Power University, Beijing, China, in 2017. Her research interests include time series analysis, process monitoring and fault diagnosis

    CHEN Jun-Hao Ph. D. Ph. D. candidate at the College of Control Science and Engineering, Zhejiang University, since 2019. He received the B.Eng. degree in mechatronic engineering from the School of Mechanical Engineering, Zhejiang University, Hangzhou, China, in 2019. His current research interests include process monitoring and pattern recognition

  • 摘要: 大容量、高参数、低能耗的百万千瓦超超临界机组是燃煤发电领域的重大装备, 已成为全国电力工业发展的主流方向, 其安全可靠运行对推动发电企业转型升级具有重要意义. 本文从分析以百万千瓦超超临界机组为代表的燃煤发电装备的本质特性出发, 揭示了其变负荷深度调峰导致的非平稳运行特性和全流程复杂耦合特性, 总结了燃煤发电过程区别于一般连续过程的问题, 指出了研究燃煤发电装备运行工况监控算法的必要性. 进而, 基于这些特性, 我们对面向燃煤发电装备工况监控的数据驱动算法近30年的发展进行回顾和分析, 展示了算法发展的不同阶段. 在此基础上, 梳理了目前燃煤发电装备工况监控中存在的问题, 并进一步介绍了燃煤发电装备工况监控未来可能的发展方向.
  • 图  1  发电过程能量转化简图((虚线红框内为热力系统的能量转化过程)

    Fig.  1  Energy conversion diagram of power generation process (Energy conversion process of thermal system is shown in the dotted box)

    图  2  百万千瓦超超临界机组非平稳运行轨迹示意图

    Fig.  2  Schematic of non-stationary operation trajectory of 1000 MW ultra-supercritical unit (a) Load curve (b) Other parameter curve

    图  3  百万千瓦超超临界机组全流程运行示意图

    Fig.  3  Schematic of the full-condition operation of 1000 MW ultra-supercritical unit

    图  4  磨煤机磨辊磨损与空预器磨损的故障表征示意图

    Fig.  4  Diagram of fault characterization of coal mill roller wear and air preheater wear

    图  5  条件轴数据重组(从时间轴非平稳数据到条件轴不同条件片)

    Fig.  5  Reorganization of a three-way data array (each gray slice represents the data samples within the same condition interval, termed condition slice)

    图  6  不同运行条件上的过程动态变化示意图

    Fig.  6  Illustration of the wide-range nonstationary and transient process

    图  7  发电设备运行工况监控方法发展趋势示意图

    Fig.  7  Schematic of development trend of operation monitoring methods of power generation equipment

    表  1  基于解析模型和基于数据驱动的发电装备工况监控方法总结

    Table  1  Summary of operation monitoring methods for power generation equipment based on analytical model and data-driven

    类型方法原理优点缺点应用对象举例
    基于解
    析模型
    数学模型
    的方法
    建立精确的数学模型和可观测输入输出量构造残差信号来反映装备期望行为与实际运行模式间是否一致1)可靠, 精确
    2)模型通用性强
    3)机理解释性强
    1)领域知识需求高
    2)模型参数辨识难
    3)复杂对象耗时长
    1)基于多模型状态估计的除氧器状态监测和故障诊断[39]
    2)基于观测器残差模型的加热器性能监测[40, 41]
    3)基于简化数学模型的回热加热器在线工况监控[42]
    基于数
    据驱动
    多元统计
    的方法
    对历史过程数据进行统计分析, 比较正常样本估算得到的监控指标置信限和每个样本的监控统计量分析当前样本的运行状态1)无需假设或对参数进行经验估计
    2)降维能力强
    3)算法解释性强, 参数易调整.
    1)处理非高斯、多模态、非线性数据时, 效果较差
    2)忽视数据微小特征对结果的影响
    1)基于主元分析相关的电厂状态监测[45-47]
    2)基于向量自回归模型的设备故障预测[51, 52]
    3)基于潜空间投影的运行过程性能评估和状态监测[53, 55]
    人工智能
    的方法
    利用人工智能算法模拟和实现人类的思维和行为, 自动完成工况监控过程1)实时数据分析, 减少人工干预
    2)强大的非线性表达能力和自适应学习能力
    1)黑箱模型, 参数和模型不易理解
    2)对数据质量和规模要求高
    1)基于混合优化递归神经网络的热力系统实时预测[66]
    2)基于人工神经网络和最优变焦搜索的加热器故障诊断[67]
    下载: 导出CSV

    表  2  三类非平稳分析方法对比总结

    Table  2  Comparison and summary of three types of non-stationary analysis methods

    类型方法优点缺点应用实例
    典型非平稳分析方法信号处理方法能处理非平稳非线性信号应用对象局限于高频振动信号1)经验模态分解[85]处理非线性非平稳时间序列
    2)应用小波变换[75]对齿轮箱振动信号进行故障诊断
    自适应策略快速适应新模式, 计算效率高无法有效区分正常的变化和缓变故障1)应用递归PCA[34]进行自适应过程监测
    2)应用递归指数慢特征分析[84]进行自适应过程监测
    基于协整分析的方法模型数量少、有效时间长应用对象局限于存在协整关系的变量1)协整分析结合慢特征分析[96]进行全工况过程监测
    2)稀疏协整分析[99]进行分布式过程监测
    时间驱动的多模式分析方法统计检验或平稳性指标判断法计算效率高模式划分粗糙, 未考虑多变量间的关系1)利用统计检验[104]确定稳态工况
    2)利用稳定性因子[105]进行模式划分
    特性变化度量与模式划分策略自动划分模式模式数量大且冗余; 在线工况确认难文献[8, 57, 106, 107]指出可以根据过程变量相关关系的变化反推过程特性的变化, 从而将运行状态分为不同模式
    高斯混合模型聚类方法拟合能力强, 自动聚类需预先指定模式数量利用高斯混合模型(Gaussian mixed model, GMM)进行多工况划分进而进行故障诊断[108]
    软过渡方法模式划分更合理, 监测模型更灵敏模式划分结果复杂, 可解释性较差1)建立了一种软过渡PCA模型[109]进行过程监测
    2)文献[110]进一步发展了软时段过渡方法
    条件驱动的多模式分析方法有序条件模式划分方法从条件轴出发, 消除了非平稳问题的影响, 抓住了工况变化的本质硬划分, 在模式边界处的样本归属可能不够合理; 同时对过渡过程进行了合并简化处理文献[114]提出了多条件模式的表征理论方法, 有利于不同模式内关键信息的分析与提取, 显著提高了模型的精度, 并在百万千瓦超超临界机组上进行了验证.
    下载: 导出CSV

    表  3  动态潜投影建模方法比较

    Table  3  Comparison of dynamic latent projecting methods

    方法目标模型参数优化解静动信息
    是否分离
    是否实现降维优点缺点
    动态主元分析DPCA[133]最大化潜变量的方差1个时延参数、潜变量个数特征根分解1次否, 潜变量维度随时延增加, 可能会大于原始数据维度求解简单, 传统方法可直接使用1)无法实现动静态信息的分离
    2)所提取潜变量易受静态信息主导
    动态潜变量模型DLV[135]最大化潜变量线性组合的方差1个时延参数、潜变量个数迭代求解, 每次找到一个潜变量具有降维能力1)无法实现动态与静态信息的分离
    2)所提取潜变量易受静态信息主
    动态内部主元分析DiPCA[136]最大化潜变量与其预测值的协方差1个时延参数、潜变量个数迭代求解, 每次找到一个潜变量具有降维能力, 且实现了动静态信息的分开监测未直接按照时序性强弱提取潜变量
    状态空间模型CVA[137]最大化潜变量的时序相关性2个时延参数、潜变量个数奇异值分解1次否, 潜变量维度随时延增加, 可能会大于原始数据维度求解简单, 实现了动静态信息的分开监测数据共线性时求解不稳定
    慢特征分析SFA[139]最大化潜变量的变化速度, 即一阶自相关性潜变量个数特征根分解2次具有降维能力, 实现对数据变化速度的表征只关注了数据的一阶时序相关性;
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-11-29
  • 修回日期:  2021-06-01
  • 网络出版日期:  2021-06-27

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