Identification of boiler reheat steam temperature model based on improved particle swarm optimization algorithm

ZHANG Jingwei; GUI Yishu; KANG Yingwei; CHEN Huanle; YANG Ping; WU Zhoujing

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Identification of boiler reheat steam temperature model based on improved particle swarm optimization algorithm

更新时间：2025-09-28

- Identification of boiler reheat steam temperature model based on improved particle swarm optimization algorithm
- Thermal Power Generation Vol. 46, Issue 7, Pages: 72-78(2017)
- 作者机构：
  
  1. 上海电力学院自动化工程学院
  2. 上海明华电力技术工程有限公司
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18;TM621.2
- Published：2017
- 稿件说明：
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ZHANG Jingwei, GUI Yishu, KANG Yingwei, et al. Identification of boiler reheat steam temperature model based on improved particle swarm optimization algorithm[J]. Thermal power generation, 2017, 46(7): 72-78.
DOI：

ZHANG Jingwei, GUI Yishu, KANG Yingwei, et al. Identification of boiler reheat steam temperature model based on improved particle swarm optimization algorithm[J]. Thermal power generation, 2017, 46(7): 72-78. DOI：

摘要

由于火电机组现场干扰及再热蒸汽温度本身特征的复杂性

传统辨识方法与基本粒子群(PSO)算法在再热蒸汽温度模型辨识方面均存在收敛速度较慢和寻优精度较低的问题。本文采用一种基于自然选择和自适应动态权重的粒子群优化(D-SPSO)算法

该算法较好地平衡了PSO算法的全局搜索能力和局部改良能力

提高了寻优性能

并且相比基本PSO算法未引入新的控制参数

避免了待调参数增多对模型辨识精度造成的负面影响。通过基于两种算法的仿真数据和超超临界660 MW机组压力直流锅炉现场数据对基本PSO算法和D-SPSO算法的再热蒸汽温度传递函数模型进行验证

结果表明

D-SPSO算法辨识得到的再热蒸汽温度更接近实际值

且寻优性能和搜索能力均有显著提升。

Abstract

Due to the field disturbance of thermal power unit and the complexity of reheat steam temperature

both the conventional identification methods and basic particle swarm optimization(PSO) algorithm have inadequacies of convergence speed and optimization precision in reheat steam temperature model's identification. A particle swarm optimization algorithm based on natural selection and adaptive dynamic weight(D-SPSO) was adopted. The algorithm balanced global searching ability and local improvement ability of the PSO algorithm

and improved the searching performance. Moreover

compared with the basic PSO algorithm

the D-SPSO algorithm didn't introduce new control parameters

avoiding the negative effect of additional parameters on model identification precision. The reheat steam temperature transfer function models of basic PSO algorithm and D-SPSO algorithm were verified by simulation data and an ultra-supercritical 660 MW pressure once-through boiler's on-site operation data. The results show that

the reheat steam temperature obtained by the D-SPSO algorithm was closer to the actual value. Besides

the optimization performance and search capability were significantly improved.

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