Data-driven Operating Monitoring for Coal-fired Power Generation Equipment: The State of the Art and Challenge
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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年的发展进行了回顾和分析, 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.Abstract: As major equipment in coal-fired power generation, 1000 MW ultra-supercritical unit has advantages of large capacity, high parameter and low energy consumption, which has become the mainstream of the development of the power industry in China. Its safety and reliability in operation are of great significance to promote the transformation and upgrading of power generation enterprises. Starting from the analysis of essential characteristics of coal-fired power generation equipment, this article revealed the nonstationary operation characteristics caused by the variable load, deep peak shaving, and the complex correlation characteristics of the whole process. Then, it summarized the problems that the coal-fired power generation process is different from general continuous processes, and points out the necessity of studying monitoring algorithms for coal-fired power generation equipment. Furthermore, based on these characteristics, it reviewed and analyzed the development of the data-driven algorithms for coal-fired power generation equipment monitoring in the past 30 years, showing different stages of algorithm development. On this basis, this article presented the current problems in operation monitoring of coal-fired power generation equipment, and further introduced the possible development direction in the future.
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图 1 发电过程能量转化简图(虚线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
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 Rearranging data according to condition axis (from nonstationary data on time axis to different condition slices on condition axis)
图 6 不同运行条件上的过程动态变化示意图
Fig. 6 Illustration of process dynamics under different operating conditions
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 基于解析模型和数据驱动的发电装备工况监控方法总结
Table 1 A comparing summary between analytical-model-based methods and data-driven methods for condition monitoring of power generation equipment
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]等数据
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以确定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]等人工智能
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]等
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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, 消除了非平稳特性的影响, 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
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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 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) 无法实现动态与静态信息的分离
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 否 是 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导动态内部主元分析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 是 是 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 是 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 是 是 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
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