Research on Condition Matching and Economic Efficiency Diagnosis Method for Thermal Power Unit Based on Just-in-time Learning

LIU Wei; REN Shaojun; SI Fengqi

doi:10.19805/j.cnki.jcspe.2025.240170

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Research on Condition Matching and Economic Efficiency Diagnosis Method for Thermal Power Unit Based on Just-in-time Learning

更新时间：2025-10-16

- Research on Condition Matching and Economic Efficiency Diagnosis Method for Thermal Power Unit Based on Just-in-time Learning
- Chinese Journal of Power Engineering Vol. 45, Issue 5, Pages: 796-804(2025)
- 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：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240170
  CLC： TM621
- Published Online：19 May 2025，
  
  Published：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.

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references

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