Dimensionality Reduction Design of Operating Conditions for Utility Boiler Combustion Adjustment Tests

YANG Xueting; LI Jinjing; JIANG Long; LU Jiayi; CHENG Liang; WU Zhenxin; GUO Hongyi; LÜ Junfu

doi:10.19805/j.cnki.jcspe.2026.250263

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Dimensionality Reduction Design of Operating Conditions for Utility Boiler Combustion Adjustment Tests

更新时间：2026-07-16

- Dimensionality Reduction Design of Operating Conditions for Utility Boiler Combustion Adjustment Tests
- Chinese Journal of Power Engineering Vol. 46, Issue 7, Pages: 185-192(2026)
- 作者机构：
  
  1. 华北电力科学研究院有限责任公司,北京,100045
  2. 清华大学能源与动力工程系,北京,100084
  3. 东方电气集团东方锅炉股份有限公司,四川,自贡,643001
  4. 上海锅炉厂有限公司,上海,200003
  5. 内蒙古大唐国际托克托发电有限责任公司,内蒙古,呼和浩特,010200
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250263
  CLC： TK16
- Published：2026
- 稿件说明：
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杨雪婷, 李金晶, 姜龙, 鲁佳易, 程亮, 武振新, 郭洪义, 吕俊复. 电站锅炉燃烧调整试验的工况降维设计[J]. 动力工程学报, 2026, 46(7): 185-192.
杨雪婷, 李金晶, 姜龙, 鲁佳易, 程亮, 武振新, 郭洪义, 吕俊复. 电站锅炉燃烧调整试验的工况降维设计[J]. 动力工程学报, 2026, 46(7): 185-192. DOI： 10.19805/j.cnki.jcspe.2026.250263.

DOI： 10.19805/j.cnki.jcspe.2026.250263

摘要

为解决试验效率与数据完备性之间的矛盾

构建了对空气分级燃烧锅炉调节量间隶属与层级关系的结构化认知

开发了依据监测参数敏感度对调节量排序的方法

进一步解耦了相同隶属与层级的调节量。基于单个调节量试验数据

提出了数据趋势分析方法。结果表明:工况降维设计使燃烧调整试验的实施时机更灵活

满足"燃料"和"电力"两个市场频繁互动条件下的运行数据提质需求

对于构建自学习和自寻优的智能控制系统具有重要意义。

Abstract

To address the contradiction between test efficiency and data completeness

a structured understanding of the affiliation and hierarchical relationships among the regulating variables of air-staged combustion boilers was established. A method for ranking these regulating variables based on monitoring parameter sensitivity was developed

which further decoupled the variables sharing the same affiliation and hierarchy. Based on the test data of individual regulating variable

a data trend analysis method was proposed. Results show that the dimensionality reduction design of operating conditions provides greater flexibility in the execution timing of combustion adjustment tests. It meets the demand for enhancing operational data quality under the frequent interaction between the "fuel" and "electricity" markets

and holds significant importance for constructing self-learning and self-optimizing intelligent control systems.

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references

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