Main steam temperature control based on ant colony optimization algorithm and BP neural network

WANG Qiuping; MA Chunlin; XIAO Lingling; CUI Rui

English

您当前的位置：

首页 >

文章列表页 >

Main steam temperature control based on ant colony optimization algorithm and BP neural network

Thermal Power Generation Vol. 42, Issue 11, Pages: 64-68(2013)

更新时间：2025-09-28

- Main steam temperature control based on ant colony optimization algorithm and BP neural network
- 作者机构：
  
  东北电力大学自动化工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM621
- Published：2013
- 稿件说明：
移动端阅览
WANG Qiuping, MA Chunlin, XIAO Lingling, et al. Main steam temperature control based on ant colony optimization algorithm and BP neural network[J]. Thermal Power Generation, 2013, 42(11): 64-68.
DOI：

WANG Qiuping, MA Chunlin, XIAO Lingling, et al. Main steam temperature control based on ant colony optimization algorithm and BP neural network[J]. Thermal Power Generation, 2013, 42(11): 64-68. DOI：

摘要

针对火电机组主蒸汽温度被控对象的不确定性和大延迟、大惯性、非线性等特点

设计了一种基于粒子群(PSO)算法、蚁群(ACO)算法、BP神经网络的智能PID串级控制系统。采用PSO算法优化ACO算法的参数、信息启发式因子α、期望启发式因子β、以及改进的ACO算法对BP神经网络初始权值进行优化;采用优化后的BP神经网络算法对PID控制器参数进行在线调整

从而实现对主蒸汽温度的动态控制。以某超临界600MW机组为对象

对ACO-BP和BP神经网络PID串级主蒸汽控制系统进行仿真试验。结果表明

ACO-BP PID串级主蒸汽控制系统较BP神经网络PID串级主蒸汽温度控制系统能更有效地克服主蒸汽温度被控对象的大延迟、时变性、非线性特性

提高了主蒸汽温度的控制品质。

Abstract

Considering the uncertainty

large delay

large inertia and nonlinear property of the main steam temperature control in thermal power plants

an intelligent PID cascade control system based on several calculation methods such as the particle swarm optimization(PSO)algorithm

the ant colony optimization(ACO)algorithm

and the BP neural network was designed.The PSO algorithm was used to optimize the parameters of the ACO algorithm

information heuristic factorα

and expectations heuristic factorβ.The improved ACO algorithm was employed to optimize the initial weights of the BP neural network.Then the optimized BP neural network algorithm was applied to adjust the PID parameters on-line

thus to realize dynamic control of the main steam temperature.An ultra supercritical 600MW unit was taken as an example to conduct simulation test on the ACO-BP and BP neural network PID cascade control system.The results show that this system outperforms conventional PID control systems in control quality and robustness.

关键词

Keywords

references

Views

421

下载量

CSCD

Alert me when the article has been cited

提交

Publicity Resources

Simulation of fuel control in fast cut back process of thermal power units

Real-time prediction model for carbon emission using BP neural network based on clustering algorithm and Bayesian optimization

Research progress on waste heat utilization technology in thermal power units

Design and implementation of intelligent water island control system for thermal power units

Control strategy for automatic parallel operation of feed water pumps in a 630 MW coal-fired unit

Related Author

DENG Tongtian

WANG Di

ZHOU Yunlong

姜宇恒

乔宗良

李逗

任少君

司风琪

Related Institution

Guizhou Electric Power Test and Research Institute

School of Energy and Power Engineering, Northeast Electric Power University

Xi’an Thermal Power Research Institute Co., Ltd.

State Grid Hunan Electric Power Company Research Institute

Hunan Xiangdian Test and Research Institute Co. Ltd.

AI问答

⁰