Soft-sensing of turbine steam flow based on CQGAPSO

汤奇峰

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Soft-sensing of turbine steam flow based on CQGAPSO

更新时间：2025-12-10

- Soft-sensing of turbine steam flow based on CQGAPSO
- CIESC Journal Issue 11, (2010)
- 作者机构：
  
  华东理工大学化工过程先进控制和优化技术教育部重点实验室
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 November 2010，
  
  Published：05 November 2010
- 稿件说明：
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汤奇峰. Soft-sensing of turbine steam flow based on CQGAPSO[J]. CIESC Journal, 2010, (11).
DOI：

汤奇峰. Soft-sensing of turbine steam flow based on CQGAPSO[J]. CIESC Journal, 2010, (11). DOI：

摘要

透平蒸汽流量是分析透平运行效率的重要参数，由于现场大量缺乏检测信息，且针对传统测量方法存在可靠性差，非接触式测量成本高、安装困难等问题，提出了一种协同量子粒子算法(CQGAPSO)，同时优化神经网络(NN)结构和参数的透平蒸汽流量的软测量建模方法。该方法利用节点间的连接开关，有效消除冗余连接对神经网络逼近能力的影响，引入量子概率幅编码和协同机制来提高神经网络的学习效率、逼近精度和泛化能力。透平蒸汽流量软测量的仿真结果表明：相比全连接神经网络和其他模型，所述方法具有更好的预测精度和鲁棒性。

Abstract

Turbine steam flow is an important parameter for analyzing turbine operating efficiency. In order to solve such problems as lack of detection information

poor reliability of traditional measurement method

high cost and difficult installation of non-contact measurement method etc.

a soft-sensing model based on the cooperative quantum genetic algorithm particle swarm optimization (CQGAPSO) and a well-defined part-connected neural network (NN) was proposed. It could eliminate some adverse effects of approximation ability caused by redundant structure of NN. The amplitude-based coding method and cooperation mechanism improved the learning efficiency

approximation precision and generalization of NN. The soft-sensing experimental results of turbine steam flow showed that the soft-sensing model of turbine steam flow could obtain higher prediction accuracy and greater robustness than the all-connection NN and other models.

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