基于样本熵的燃气-蒸汽联合循环机组稳态判定  

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

作　　者：孔羽[1] 任少君[1] 司风琪[1] 黄志军 徐治皋[1] 

机构地区：[1]东南大学能源热转换及其过程测控教育部重点实验室,江苏南京210096 [2]大唐苏州热电有限责任公司,江苏吴江215200

出　　处：《热力发电》2016年第4期28-34,共7页Thermal Power Generation

基　　金：国家自然科学基金项目(51176030)

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。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.

关 键 词：稳态判断 燃气 蒸汽 联合循环 神经网络 样本熵 稳态因子 信噪比 

分 类 号：TM611[电气工程—电力系统及自动化]