Optimization of Variable Load Rate for Coal-fired Units Based on AM-RFR-PSO Hybrid Algorithm

ZHAO Zichun; LI Xiaobo; CHEN Heng; REN Shuo; YAO Lei; LI Yu; ZHANG Junjiao

doi:10.19805/j.cnki.jcspe.2026.250141

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Optimization of Variable Load Rate for Coal-fired Units Based on AM-RFR-PSO Hybrid Algorithm

更新时间：2026-07-16

- Optimization of Variable Load Rate for Coal-fired Units Based on AM-RFR-PSO Hybrid Algorithm
- Chinese Journal of Power Engineering Vol. 46, Issue 7, Pages: 152-160(2026)
- 作者机构：
  
  1. 华北电力大学能源动力与机械工程学院,北京,102206
  2. 青海电力交易中心有限公司,青海,西宁,810008
  3. 华能岳阳发电有限责任公司,湖南,岳阳,414002
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250141
  CLC： TK37
- Published：2026
- 稿件说明：
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赵子淳, 李晓博, 陈衡, 任硕, 姚磊, 李昱, 张俊姣. 基于AM-RFR-PSO混合算法的燃煤机组变负荷速率优化[J]. 动力工程学报, 2026, 46(7): 152-160.
赵子淳, 李晓博, 陈衡, 任硕, 姚磊, 李昱, 张俊姣. 基于AM-RFR-PSO混合算法的燃煤机组变负荷速率优化[J]. 动力工程学报, 2026, 46(7): 152-160. DOI： 10.19805/j.cnki.jcspe.2026.250141.

DOI： 10.19805/j.cnki.jcspe.2026.250141

摘要

在深度调峰背景下

火电机组出现升降负荷速率慢、无法及时响应调峰要求的问题

为提高机组变负荷速率

提出了一种融合注意力机制的随机森林-粒子群混合算法(AM-RFR-PSO)的燃煤机组运行优化方法。首先使用相关性分析选出与机组输出功率相关的运行参数作为模型特征变量

引入注意力机制对随机森林算法进行改进

使用改进后的算法对火电机组输出功率进行预测建模

并与其他算法进行对比

最后使用粒子群算法对主要特征变量进行优化

提高火电机组的升降负荷速率。结果表明:与其他算法相比

AM-RFR预测模型的均方根误差、平均绝对误差最低

回归系数最高

证明该模型的预测精度较高

经过PSO优化后的运行参数可以使机组的升降负荷速率提升接近5%

可以有效提升机组的响应速度。

Abstract

Under the deep peak regulation background

thermal power units encounter such problems as slow load variation rate and inability to respond promptly to peak regulation requirements. To improve the unit's variable load rate

an operational optimization method for coal-fired power units based on a hybrid random forest-particle swarm optimization algorithm incorporating an attention mechanism (AM-RFR-PSO) was proposed. Firstly

correlation analysis was used to select the operation parameters related to unit output power as the model feature variables. Then attention mechanism was introduced to improve the random forest algorithm

and the improved algorithm was used to model the output power prediction of thermal power unit and compared with other algorithms. Finally

particle swarm algorithm was used to optimize the main feature variables to improve the load variation rate of the thermal power unit. The results show that compared with other algorithms

the AM-RFR prediction model has the smallest root mean square error

average absolute error

and the highest regression coefficient

which proves that the prediction model has high prediction accuracy

and the PSO-optimized operating parameters can improve the unit's variable load rate by close to 5%

which can effectively improve the unit's response speed.

关键词

Keywords

references

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