State stability determination of gas-steam combined cycle unit based on sample entropy

KONG Yu; REN Shaojun; SI Fengqi; HUANG Zhijun; XU Zhigao

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State stability determination of gas-steam combined cycle unit based on sample entropy

更新时间：2025-09-28

- State stability determination of gas-steam combined cycle unit based on sample entropy
- Thermal Power Generation Vol. 45, Issue 4, Pages: 28-34(2016)
- 作者机构：
  
  1. 东南大学能源热转换及其过程测控教育部重点实验室
  2. 大唐苏州热电有限责任公司
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM611.31
- Published：2016
- 稿件说明：
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KONG Yu, REN Shaojun, SI Fengqi, et al. State stability determination of gas-steam combined cycle unit based on sample entropy[J]. Thermal power generation, 2016, 45(4): 28-34.
DOI：

KONG Yu, REN Shaojun, SI Fengqi, et al. State stability determination of gas-steam combined cycle unit based on sample entropy[J]. Thermal power generation, 2016, 45(4): 28-34. DOI：

摘要

本文提出了一种基于样本熵的燃气-蒸汽联合循环机组稳态判断方法

利用样本熵对信号的趋势变化及波动幅度的敏感性

获取目标序列的稳态因子

从而进行海量数据的筛选与归类。对几种典型仿真信号进行样本熵分析

讨论样本熵与信噪比的关系。并将样本熵用于燃气蒸汽联合循环重要参数稳态运行数据的优选。结果表明:样本熵对信号的趋势变化及波动幅度的识别能力较高

利用样本熵进行稳态判断可行;利用该优选的样本数据进行燃机特性的神经网络建模

有效提高了模型精度

大大降低了模型测试的误诊率。

Abstract

On the basis of sample entropy

this paper proposed a method for state stability analysis of gassteam combined cycle units.In this method

the sample entropy's sensitivity to fluctuation amplitude and change trend of signals was used to obtain the steady factor of the target sequences

thus to carry out selection and classification of the massive data.The sample entropies of several typical simulation signals were calculated respectively

and the relationship between sample entropy and signal-to-noise ratio(SNR)was discussed

which verified the sample entropy's good recognition ability to unsteady signals.Moreover

this method was applied in the extraction of steady data of part parameters in a gas-steam combined cycle

and the selected samples were used for neural network modeling of the gas-steam combined cycle unit

which improved the accuracy of the network model and decreased the misdiagnosis rate of test.

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