基于即时学习的火电机组工况匹配与经济性诊断方法研究

刘维; 任少君; 司风琪

doi:10.19805/j.cnki.jcspe.2025.240170

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基于即时学习的火电机组工况匹配与经济性诊断方法研究

综合能源系统 | 更新时间：2025-10-16

- 基于即时学习的火电机组工况匹配与经济性诊断方法研究
- Research on Condition Matching and Economic Efficiency Diagnosis Method for Thermal Power Unit Based on Just-in-time Learning
- 动力工程学报 2025年45卷第5期页码：796-804
- 作者机构：
  
  东南大学能源热转换及过程测控教育部重点实验室,江苏,南京,210096
- 作者简介：
  
  [ "刘维(1999—),男,河南信阳人,硕士研究生,研究方向为火电机组运行优化与故障诊断,E-mail:wliuseu@126.com" ]
- 基金信息：
  
  国家重点研发计划资助项目(2022YFB4100700)
- DOI：10.19805/j.cnki.jcspe.2025.240170
  中图分类号： TM621
- 网络出版：2025-05-19，
  
  纸质出版：2025
- 稿件说明：
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刘维,任少君,司风琪. 基于即时学习的火电机组工况匹配与经济性诊断方法研究动力工程学报, 2025, 45(5): 796-804 https://doi.

org/10.19805/j.cnki.jcspe.2025.240170
刘维,任少君,司风琪. 基于即时学习的火电机组工况匹配与经济性诊断方法研究动力工程学报, 2025, 45(5): 796-804 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240170.

org/10.19805/j.cnki.jcspe.2025.240170 DOI：

摘要

现有基于数据驱动的能耗优化与节能诊断方法大多基于离线全局模型进行分析

在实际运行中复杂多样的机组状态会使所建模型性能下降。针对该问题

本文提出一种基于即时学习和工况匹配的机组经济性诊断方法。在机组历史运行工况数据的基础上进行数据预处理

选取与目标参数相关的边界参数和相关参数。通过网格划分得到不同边界下的工况单元

并筛选性能最优样本作为基准工况样本集

建立目标工况库。在目标工况库的基础上进行工况匹配

如果成功匹配

则选取对应边界下性能最优值作为参数基准值

反之采用即时学习方法

通过相似度度量提取相似工况样本

建立局部在线模型

将输出预测值作为基准值

作为性能评价和控制调整的参考。结果表明:根据所提方法能够获得不同工况单元内厂用电率基准值

得到脱硫耗电率的优化策略

为机组运行优化提供了可靠指导。

Abstract

Most existing data-driven energy optimization and energy-saving diagnostic methods were based on offline global models for analysis. Complex and variable states of the units during actual operation could degrade the performance of these models. Regarding this issue

a method for economic efficiency diagnosis of the unit was proposed based on just-in-time learning (JITL) and condition matching. This method involved preprocessing historical operational data of the unit

selecting boundary parameters and relevant parameters related to the target parameter. The conditions were divided into different cells under various boundaries through grid partitioning and the samples with optimal performance were selected as the benchmark condition sample set

and the target condition database was established. Condition matching was conducted based on this database. If the matching was successful

the optimal performance value under the corresponding boundary was selected as the parameter benchmark. Otherwise

a JITL approach was employed

which extracted similar condition samples through similarity measurement

and established a local online model to output the predicted value as the benchmark for performance evaluation and control adjustment. Results show that benchmark values of plant electricity consumption rate for different operating conditions and optimization strategies for desulfurization electricity consumption rate can be obtained based on the proposed method

providing reliable guidance for unit operation optimization.

关键词

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references

弓林娟, 王文毓, 高耀岿, 等. 面向大规模新能源消纳的火电机组平行控制[J]. 动力工程学报, 2023, 43(2): 136-142. GONG Linjuan, WANG Wenyu, GAO Yaokui, et al. Parallel control of thermal power unit for large-scale renewable energy accommodation[J]. Journal of Chinese Society of Power Engineering, 2023, 43(2): 136-142.

国家发展改革委, 国家能源局. "十四五"现代能源体系规划[R]. 北京: 国家发展改革委, 国家能源局, 2022.

刘吉臻, 李云鸷, 宋子秋, 等. 灵活智能燃煤发电技术及评价体系[J]. 动力工程学报, 2022, 42(11): 993-1004, 1012. LIU Jizhen, LI Yunzhi, SONG Ziqiu, et al. Flexible and intelligent coal-fired power generation technology and its evaluation system[J]. Journal of Chinese Society of Power Engineering, 2022, 42(11): 993-1004, 1012.

彭浩. 基于神经网络的火电机组运行诊断优化方法研究[D]. 北京: 华北电力大学, 2018.

蔡雨. 基于大数据挖掘的火电机组能耗特性分析及诊断研究[D]. 杭州: 浙江大学, 2018.

杨云溪. 基于数据驱动的火电厂能耗诊断方法与分析研究[D]. 北京: 华北电力大学, 2021.

王宁玲, 杨勇平, 杨志平. 多变边界条件下火电机组能耗基准状态诊断[J]. 中国电机工程学报, 2013, 33(26): 1-8. WANG Ningling, YANG Yongping, YANG Zhiping. Energy-consumption benchmark diagnosis of thermal power units under varying operation boundary[J]. Proceedings of the CSEE, 2013, 33(26): 1-8.

GE Zhiqiang, SONG Zhihuan. A comparative study of just-in-time-learning based methods for online soft sensor modeling[J]. Chemometrics and Intelligent Laboratory Systems, 2010, 104(2): 306-317.

王亚欧, 陈有福, 牟柯昱, 等. 火电机组能耗指标体系构建与经济性诊断研究[J]. 热能动力工程, 2022, 37(8): 175-185. WANG Yaou, CHEN Youfu, MOU Keyu, et al. Study on index system construction of energy consumption and economic diagnosis of thermal power unit[J]. Journal of Engineering for Thermal Energy and Power, 2022, 37(8): 175-185.

江浩, 徐治皋. 电站运行优化决策支持系统及优化值的确定[J]. 动力工程, 2003, 23(3): 2480-2484, 2376. JIANG Hao, XU Zhigao. Study of operation optimization decision support system (OODSS) for power plants and decision of optimization value[J]. Power Engineering, 2003, 23(3): 2480-2484, 2376.

LALL S, SINHA D, GHOSH A, et al. Stable feature selection using copula based mutual information[J]. Pattern Recognition, 2021, 112: 107697.

USHIDA S, KIMURA H. Just-in-time approach to nonlinear identification and control[J]. Journal of the Society of Instrument and Control Engineers, 2005, 44(2): 102-106.

袁小锋. 基于即时学习的复杂非线性过程软测量建模及应用[D]. 杭州: 浙江大学, 2016.

李建强, 杨红, 牛成林. 基于改进即时学习算法的吸收塔pH值软测量技术研究[J]. 电力科学与工程, 2021, 37(8): 60-66. LI Jianqiang, YANG Hong, NIU Chenglin. Research on soft sensor technology of the pH value of absorption tower based on improved just-in-time learning algorithm[J]. Electric Power Science and Engineering, 2021, 37(8): 60-66.

BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.

李兵, 韩睿, 何怡刚, 等. 改进随机森林算法在电机轴承故障诊断中的应用[J]. 中国电机工程学报, 2020, 40(4): 1310-1319, 1422. LI Bing, HAN Rui, HE Yigang, et al. Applications of the improved random forest algorithm in fault diagnosis of motor bearings[J]. Proceedings of the CSEE, 2020, 40(4): 1310-1319, 1422.

周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.

田书耘. 基于大数据的厂用电系统分析与模型研究[D]. 南京: 东南大学, 2018.

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