基于信号分解和统计假设检验的稳态检测方法
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摘要
稳态检测对热工过程非常重要,并在建模、优化和控制中具有广泛的应用。针对热工过程提出一种基于信号分解和统计学理论的稳态检测方法,该方法首先利用经验小波变换(EWT)这一非参数信号分解技术对热工过程信号进行分解,然后进行统计假设检验。在所提出的方法中,首先对采样数据的傅里叶谱特性进行自适应分割以获得热工过程整体运行趋势,通过对中高频信息进行信号重建获得过程的震荡信息。然后通过使用修改过的R统计检验法来对热工过程的稳定性进行检测。最后以某电厂1 000 MW机组协调系统的历史数据进行稳态检测实验,验证了该方法的有效性。
Steady state detection is very important for thermal processes and it is widely used in modeling, optimization and control. In this paper, a steady-state detection method based on signal decomposition and statistical theory is proposed for thermal processes, which utilizes a nonparametric signal decomposition technique named empirical wavelet transform(EWT) to decompose thermal process signal and then conducts a statistical hypothesis test. In the proposed method, firstly, the Fourier spectrum characteristic of the sampled data is adaptively divided to obtain the overall running trend of the thermal process, and the oscillation information of the process is obtained by performing signal reconstruction on the intermediate-high frequency information. The modified R-statistic test method is used to test the stability of the thermal process. Finally, a steady-state detection experiment was conducted with the historical data of the 1 000 MW unit coordinated control system in a certain power plant, which verifies the effectiveness of the proposed method.
Steady state detection is very important for thermal processes and it is widely used in modeling, optimization and control. In this paper, a steady-state detection method based on signal decomposition and statistical theory is proposed for thermal processes, which utilizes a nonparametric signal decomposition technique named empirical wavelet transform(EWT) to decompose thermal process signal and then conducts a statistical hypothesis test. In the proposed method, firstly, the Fourier spectrum characteristic of the sampled data is adaptively divided to obtain the overall running trend of the thermal process, and the oscillation information of the process is obtained by performing signal reconstruction on the intermediate-high frequency information. The modified R-statistic test method is used to test the stability of the thermal process. Finally, a steady-state detection experiment was conducted with the historical data of the 1 000 MW unit coordinated control system in a certain power plant, which verifies the effectiveness of the proposed method.
引文
[1] VAZQUEZ L, BLANCO J M, RAMIS R, et al. Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring[J]. Energy, 2015, 93(12):923-944.
[2] BEJARANO G, RODRIGUEZ D, ALFAYA J A, et al. On identifying steady-state parameters of an experimental mechanical-compression refrigeration plant[J]. Applied Thermal Engineering, 2016, 109(8):318-333.
[3] TALEBI S S, TOUSI A M. The effects of compressor blade roughness on the steady state performance of micro-turbines[J]. Applied Thermal Engineering, 2016, DOI:10.1016/j.applthermaleng.2016.12.038.
[4] SU Y G, ZHANG H Y, WANG Z H, et al. Steady-state load identification method of inductive power transfer system based on switching capacitors[J]. IEEE Transactions on Power Electronics, 2015, 30(11): 6349-6355.
[5] MARTINEZ-MARADIAGA D, BRUNO J C, CORONAS A. Steady-state data reconciliation for absorption refrigeration systems[J]. Applied Thermal Engineering, 2013, 51(1-2):1170-1180.
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[8] 刘吉臻,高萌,吕游,等. 过程运行数据的稳态检测方法综述[J]. 仪器仪表学报, 2013, 34(8):1739-1748.LIU J ZH, GAO M, LV Y, et al. Overview on the steady-state detection methods of process operating data[J]. Chinese Journal of Scientific Instrument, 2013, 34(8):1739-1748.
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[10] CAO S, RHINEHART R R. An efficient method for on-line identification of steady state[J]. Journal of Process Control, 1995, 5(6):363-374.
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[12] 高萌,刘吉臻,王瑞琪,等. 基于自适应高斯滤波的电站历史数据稳态检测方法[J]. 动力工程学报, 2014, 34(9):708-713.GAO M, LIU J ZH, WANG R Q, et al. Steady-state detection of power plant history data based on adaptive gauss filter[J]. Journal of Power Engineering, 2014, 34(9):708-713.
[13] 毕小龙,王洪跃,司风琪,等. 基于趋势提取的稳态检测方法[J]. 动力工程学报, 2006, 26(4):503-506.BI X L, WANG H Y, SI F Q, et al. A method based on tendency distillation for ascertaining state steadiness[J]. Journal of Power Engineering, 2006, 26(4):503-506.
[14] 吕游,刘吉臻,赵文杰,等. 基于分段曲线拟合的稳态检测方法[J]. 仪器仪表学报, 2012, 33(1):194-200.LV Y, LIU J ZH, ZHAO W J, et al. Steady-state detecting method based on piecewise curve fitting[J]. Chinese Journal of Scientific Instrument, 2012, 33(1): 194-200.
[15] KELLY J D, HEDENGREN J D. A steady-state detection (SSD) algorithm to detect non-stationary drifts in processes[J]. Journal of Process Control, 2013, 23(3):326-331.
[16] KORBEL M, BELLEC S, JIANG T, et al. Steady state identification for on-line data reconciliation based on wavelet transform and filtering[J]. Computers & Chemical Engineering, 2014, 63(11):206-218.
[17] GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010.
[18] 肖启阳,李健,孙洁娣,等. 基于EWT及互时频的天然气管道泄漏定位[J]. 仪器仪表学报, 2016, 37(12): 2735-2742.XIAO Q Y, LI J, SUN J D, et al. Pipeline leak location based on EWT and CTFS[J]. Chinese Journal of Scientific Instrument, 2016, 37(12):2735-2742.
[19] 祝文颖,冯志鹏. 基于改进经验小波变换的行星齿轮箱故障诊断[J]. 仪器仪表学报, 2016, 37(10): 2193-2201.ZHU W Y, FENG ZH P. Fault diagnosis of planetary gearbox based on improved empirical wavelet transform[J]. Chinese Journal of Scientific Instrument, 2016, 37(10): 2193-2201.
[20] 刘春,谢皓,肖奕霖,等. EWT算法在ECG信号滤波中的研究[J]. 电子测量与仪器学报, 2017,31(11): 1835-1842.LIU CH, XIE H, XIAO Y L, et al. Research on empirical wavelet transform algorithm in ECG signal filtering[J]. Journal of Electronic Measurement and Instrumentation, 2017, 31(11):1835-1842.
[21] 徐元博,蔡宗琰. 三点对称差分能量算子与经验小波变换在轴承故障诊断中的应用[J]. 电子测量与仪器学报, 2017, 31(8):1247-1256.XU Y B, CAI Z Y. Application of demodulation energy operator of symmetrical differencing and empirical wavelet transform in bearing fault diagnosis[J]. Journal of Electronic Measurement and Instrumentation, 2017, 31(8):1247-1256.
[22] 袁世通. 1000 MW超超临界机组建模理论与方法的研究[D]. 北京:华北电力大学,2015.YUAN SH T. Research on modeling theory and method for 1000 MW ultra supercritical unit[D]. Beijing:North China Electric Power University,2015.
[2] BEJARANO G, RODRIGUEZ D, ALFAYA J A, et al. On identifying steady-state parameters of an experimental mechanical-compression refrigeration plant[J]. Applied Thermal Engineering, 2016, 109(8):318-333.
[3] TALEBI S S, TOUSI A M. The effects of compressor blade roughness on the steady state performance of micro-turbines[J]. Applied Thermal Engineering, 2016, DOI:10.1016/j.applthermaleng.2016.12.038.
[4] SU Y G, ZHANG H Y, WANG Z H, et al. Steady-state load identification method of inductive power transfer system based on switching capacitors[J]. IEEE Transactions on Power Electronics, 2015, 30(11): 6349-6355.
[5] MARTINEZ-MARADIAGA D, BRUNO J C, CORONAS A. Steady-state data reconciliation for absorption refrigeration systems[J]. Applied Thermal Engineering, 2013, 51(1-2):1170-1180.
[6] ABEYSEKERA M, WU J, JENKINS N, et al. Steady state analysis of gas networks with distributed injection of alternative gas[J]. Applied Energy, 2016, 164(6):991-1002.
[7] RHINEHART R R. Automated steady and transient state identification in noisy processes[C]. American Control Conference, 2013:4477-4493.
[8] 刘吉臻,高萌,吕游,等. 过程运行数据的稳态检测方法综述[J]. 仪器仪表学报, 2013, 34(8):1739-1748.LIU J ZH, GAO M, LV Y, et al. Overview on the steady-state detection methods of process operating data[J]. Chinese Journal of Scientific Instrument, 2013, 34(8):1739-1748.
[9] NEUMANN J V. Distribution of the ratio of the mean square successive difference to the variance[J]. Annals of Mathematical Statistics, 1941, 12(4):367-395.
[10] CAO S, RHINEHART R R. An efficient method for on-line identification of steady state[J]. Journal of Process Control, 1995, 5(6):363-374.
[11] HUANG B L, YAO Y. Automatic steady state identification for batch processes by nonparametric signal decomposition and statistical hypothesis test[J]. Chemometrics & Intelligent Laboratory Systems, 2014, 138(11):84-96.
[12] 高萌,刘吉臻,王瑞琪,等. 基于自适应高斯滤波的电站历史数据稳态检测方法[J]. 动力工程学报, 2014, 34(9):708-713.GAO M, LIU J ZH, WANG R Q, et al. Steady-state detection of power plant history data based on adaptive gauss filter[J]. Journal of Power Engineering, 2014, 34(9):708-713.
[13] 毕小龙,王洪跃,司风琪,等. 基于趋势提取的稳态检测方法[J]. 动力工程学报, 2006, 26(4):503-506.BI X L, WANG H Y, SI F Q, et al. A method based on tendency distillation for ascertaining state steadiness[J]. Journal of Power Engineering, 2006, 26(4):503-506.
[14] 吕游,刘吉臻,赵文杰,等. 基于分段曲线拟合的稳态检测方法[J]. 仪器仪表学报, 2012, 33(1):194-200.LV Y, LIU J ZH, ZHAO W J, et al. Steady-state detecting method based on piecewise curve fitting[J]. Chinese Journal of Scientific Instrument, 2012, 33(1): 194-200.
[15] KELLY J D, HEDENGREN J D. A steady-state detection (SSD) algorithm to detect non-stationary drifts in processes[J]. Journal of Process Control, 2013, 23(3):326-331.
[16] KORBEL M, BELLEC S, JIANG T, et al. Steady state identification for on-line data reconciliation based on wavelet transform and filtering[J]. Computers & Chemical Engineering, 2014, 63(11):206-218.
[17] GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010.
[18] 肖启阳,李健,孙洁娣,等. 基于EWT及互时频的天然气管道泄漏定位[J]. 仪器仪表学报, 2016, 37(12): 2735-2742.XIAO Q Y, LI J, SUN J D, et al. Pipeline leak location based on EWT and CTFS[J]. Chinese Journal of Scientific Instrument, 2016, 37(12):2735-2742.
[19] 祝文颖,冯志鹏. 基于改进经验小波变换的行星齿轮箱故障诊断[J]. 仪器仪表学报, 2016, 37(10): 2193-2201.ZHU W Y, FENG ZH P. Fault diagnosis of planetary gearbox based on improved empirical wavelet transform[J]. Chinese Journal of Scientific Instrument, 2016, 37(10): 2193-2201.
[20] 刘春,谢皓,肖奕霖,等. EWT算法在ECG信号滤波中的研究[J]. 电子测量与仪器学报, 2017,31(11): 1835-1842.LIU CH, XIE H, XIAO Y L, et al. Research on empirical wavelet transform algorithm in ECG signal filtering[J]. Journal of Electronic Measurement and Instrumentation, 2017, 31(11):1835-1842.
[21] 徐元博,蔡宗琰. 三点对称差分能量算子与经验小波变换在轴承故障诊断中的应用[J]. 电子测量与仪器学报, 2017, 31(8):1247-1256.XU Y B, CAI Z Y. Application of demodulation energy operator of symmetrical differencing and empirical wavelet transform in bearing fault diagnosis[J]. Journal of Electronic Measurement and Instrumentation, 2017, 31(8):1247-1256.
[22] 袁世通. 1000 MW超超临界机组建模理论与方法的研究[D]. 北京:华北电力大学,2015.YUAN SH T. Research on modeling theory and method for 1000 MW ultra supercritical unit[D]. Beijing:North China Electric Power University,2015.