Soft measurement for thermal parameters based on correlation vector machine

ZHANG Wentao; MA Yongguang; DONG Zijian; DU Jingqi

doi:10.19666/j.rlfd.201805103

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Soft measurement for thermal parameters based on correlation vector machine

更新时间：2025-09-28

- Soft measurement for thermal parameters based on correlation vector machine
- Thermal Power Generation Vol. 48, Issue 2, Pages: 90-95(2019)
- 作者机构：
  
  1. 华北电力大学控制与计算机工程学院
  2. 云南电力试验研究院(集团)有限公司
  3. 云南电网有限责任公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.201805103
  CLC： TP18;TM621
- Published：2019
- 稿件说明：
移动端阅览
ZHANG Wentao, MA Yongguang, DONG Zijian, et al. Soft measurement for thermal parameters based on correlation vector machine[J]. Thermal power generation, 2019, 48(2): 90-95.
DOI：

ZHANG Wentao, MA Yongguang, DONG Zijian, et al. Soft measurement for thermal parameters based on correlation vector machine[J]. Thermal power generation, 2019, 48(2): 90-95. DOI： 10.19666/j.rlfd.201805103.

摘要

针对火电机组中某些重要热工参数难以直接测量的问题

本文提出了一种基于相关向量机(RVM)的热工参数软测量方法。采用组合核函数的形式代替传统的单一核函数

采用改进的鲸鱼优化算法(IWOA)优化核函数参数和组合核权重系数

利用优化后的参数和机组实际工况数据建立IWOA-RVM软测量模型

并采用样本更新策略实现该模型的在线更新。将该模型应用于某600 MW燃煤机组锅炉参数测量

结果表明:本文提出的IWOA-RVM软测量模型的烟气含氧量和飞灰含碳量预测结果均方根误差分别为0.085 2和0.088 4

均低于BP神经网络和支持向量机软测量模型;本文模型所需的相关向量少

模型稀疏性好

测试精度高

具有较强的泛化能力。

Abstract

Some important thermal parameters of thermal power units are difficult to measure directly

to solve this problem

a soft-measurement method based on correlation vector machine(RVM) was proposed. In this method

the combined kernel function replaces the conventional single kernel function

the improved whale optimization algorithm(IWOA) is used to optimize the kernel function parameters and the combined kernel weight coefficient

and the IWOA-RVM soft measurement model is established by using the optimized parameters and the actual operating conditions data of the unit. Moreover

the sample update strategy is applied to realize online update of the model. Furthermore

the above model was employed to the parameter measurement of a 600 MW coal-fired boiler.The results show that

by using the IWOA-RVM soft measurement model

the root mean square error of the predicted oxygen content in flue gas and carbon content in fly ash is 0.085 2 and 0.088 4

both are lower than that by the BP neural network and support vector machine soft measurement model. The proposed model requires less correlation vector

and has good model sparseness

high test accuracy and strong generalization ability.

关键词

Keywords

references

Views

123

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Dynamic soft measurement method for coal calorific value based on Calman filter

Online soft measurement of NO_x mass concentration for circulating fluidized bed boiler based on BP neural network

Robust least squares support vector machine based on online adaptive and its application

Prediction model of carbon content in fly ash using random forest variable selection method

Soft measurement model of primary air flow of coal mill based on self-adaptive recursive LSSVM

Related Author

ZHANG Quan

BIAN Fang

LIU Xinping

HAO Xiaohui

ZHANG Peihua

ZHU Zhujun

BAI Jianyun

LIU Xiao

Related Institution

Department of Automation, North China Electric Power University

Jingneng Kangbash Power Generation Co., Ltd

Department of Automation, Shanxi University

Shanxi Pingshuo Coal Gangue Power Generation Co., Ltd

School of Electrical and Power Engineering, Taiyuan University of Technology

AI问答

⁰