Prediction model of steam turbine heat consumption based on improved fuzzy support vector machine

HUANG Xinyu; ZHANG Dongliang; LI Shuaiwei

doi:10.19666/j.rlfd.201806119

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Prediction model of steam turbine heat consumption based on improved fuzzy support vector machine

更新时间：2025-09-28

- Prediction model of steam turbine heat consumption based on improved fuzzy support vector machine
- Thermal Power Generation Vol. 48, Issue 3, Pages: 22-27(2019)
- 作者机构：
  
  上海电力学院自动化工程学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.201806119
  CLC： TK26
- Published Online：26 February 2019，
  
  Published：2019
- 稿件说明：
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HUANG Xinyu, ZHANG Dongliang, LI Shuaiwei. Prediction model of steam turbine heat consumption based on improved fuzzy support vector machine[J]. Thermal power generation, 2019, 48(3): 22-27.
DOI：

HUANG Xinyu, ZHANG Dongliang, LI Shuaiwei. Prediction model of steam turbine heat consumption based on improved fuzzy support vector machine[J]. Thermal power generation, 2019, 48(3): 22-27. DOI： 10.19666/j.rlfd.201806119.

摘要

针对现有方法难以准确预测具有复杂非线性特征的汽轮机热耗率问题

本文提出一种改进模糊支持向量机(FSVM)的汽轮机热耗率预测模型。首先采用间隔统计算法计算热耗率数据最佳聚类个数

防止出现聚类数目的不确定性

然后利用模糊C均值聚类(KFCM)算法将热耗率数据划分

生成聚类子样本

将聚类子样本代入经粒子群算法优化的FSVM中

建立基于FSVM的汽轮机热耗率预测模型。将现场采集的某超超临界660 MW机组汽轮机热耗率数据输入模型进行预测

并与传统支持向量机的预测结果进行比较。结果表明

改进的FSVM方法具有更高的预测精度和更强的泛化能力。

Abstract

To the problem that the existing methods are difficult to accurately predict and analyze the heat consumption of steam turbines with complex non-linear characteristics

this paper presents an improved prediction model of steam turbine heat consumption based on fuzzy support vector machine(FSVM). Firstly

the gap statistic algorithm is used to calculate the optimal number of clusters to avoid the uncertainty of the number of clusters.Then

the kernel fuzzy C mean clustering(KFCM) algorithm is applied to divide the heat consumption data

generate cluster subsamples

and replace it into the FSVM optimized by particle swarm optimization(PSO)

and establish a thermal consumption rate prediction model based on the FSVM. Finally

this model is employed to predict the heat consumption rate of an ultra-supercritical 660 MW unit steam turbine based on the data collected in the field

and the results are compared with that of the conventional support vector machine. The research results show that the improved FSVM method has higher prediction accuracy and stronger generalization ability.

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