Main Steam Flowrate Measurement Model for Steam Turbines Based on KPCA-BiLSTM-GRU

SHENG Wen; XU Shiming; LU Guanfa; GAO Cheng; QI Jing; PENG Xianyong; ZHOU Huaichun

doi:10.19805/j.cnki.jcspe.2025.240453

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Main Steam Flowrate Measurement Model for Steam Turbines Based on KPCA-BiLSTM-GRU

更新时间：2025-11-28

- Main Steam Flowrate Measurement Model for Steam Turbines Based on KPCA-BiLSTM-GRU
- Chinese Journal of Power Engineering Vol. 45, Issue 9, Pages: 1483-1491(2025)
- 作者机构：
  
  1. 中国矿业大学低碳能源与动力工程学院,江苏,徐州,221116
  2. 江苏省智慧能源技术及装备工程研究中心,江苏,徐州,221116
  3. 江苏华美热电有限公司,江苏,徐州,221141
  4. 南京师范大学能源与机械工程学院,江苏,南京,210023
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240453
  CLC：
- Published Online：16 September 2025，
  
  Published：2025
- 稿件说明：
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盛稳,徐世明,卢官发,高成,祁晶,彭献永,周怀春. 基于KPCA-BiLSTM-GRU的汽轮机主蒸汽流量测量模型动力工程学报, 2025, 45(9): 1483-1491 https://doi.

org/10.19805/j.cnki.jcspe.2025.240453
盛稳,徐世明,卢官发,高成,祁晶,彭献永,周怀春. 基于KPCA-BiLSTM-GRU的汽轮机主蒸汽流量测量模型动力工程学报, 2025, 45(9): 1483-1491 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240453.

org/10.19805/j.cnki.jcspe.2025.240453 DOI：

摘要

针对难以准确监测机组主蒸汽流量的问题

提出了一种基于核主成分分析-双向长短期记忆-门控循环单元(KPCA-BiLSTM-GRU)的主蒸汽流量测量模型

并以某1 000 MW超超临界一次再热发电机组的历史数据为基础进行了仿真验证。首先

根据实际生产过程中机组运行机理及经验

选取了与主蒸汽流量相关的运行参数作为测量模型输入的候选变量;其次

利用KPCA算法对原始的候选输入特征进行降维

避免因模型输入变量过多对预测结果精度产生影响;最后

使用BiLSTM-GRU神经网络模型进一步学习输入数据特征的变化规律

实现了主蒸汽流量的回归预测。选用反向传播(BP)、LSTM、BiLSTM、GRU等神经网络模型进行了对比实验

以验证所提出模型的预测效果。结果表明:所提出的基于KPCA-BiLSTM-GRU的主蒸汽流量模型能够实现主蒸汽流量的准确测量

其均方根误差(RMSE)为25.76 t/h

平均绝对百分比误差(MAPE)为1.21%;相比于实验中其他模型

KPCA-BiLSTM-GRU主蒸汽流量测量模型的预测效果更好

对深度调峰汽轮发电机组变负荷工况有较好的适应性。

Abstract

Aiming at the problem that it is difficult to accurately measure the main steam flowrate of a unit

a main steam flowrate measurement model based on KPCA-BiLSTM-GRU was proposed

and a simulation verification was proposed based on historical data of a certain 1 000 MW ultra-supercritical primary reheat generating unit. Firstly

based on the operating mechanism and experience of the unit during actual production

operating parameters related to the main steam flowrate were selected as candidate variables for input into the measurement model. Secondly

KPCA algorithm was used to reduce the dimension of the original candidate input features

thereby avoiding the impact on the prediction accuracy caused by an excessive number of input variables in the model. Finally

the BiLSTM-GRU neural network model was used to further learn the change law of the input data features

achieving the regression prediction of the main steam flowrate. At the same time

Neural network models such as BP

LSTM

BiLSTM and GRU were selected for comparative experiments to verify the prediction performance of the proposed model. The results show that the proposed main steam flowrate model based on KPCA-BiLSTM-GRU can accurately measure the main steam flowrate. Its root mean square error (RMSE) is 25.76 t/h

and the mean absolute percentage error (MAPE) is 1.21%. Compared with other models in the experiment

the KPCA-BiLSTM-GRU main steam flowrate measurement model has a better prediction effect and is more adaptable to the variable load operation conditions of the deep load-peak shaving steam turbine power generation unit.

关键词

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references

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