Steady-state detection method based on signal decomposition and statistical hypothesis test

被引：0
|
作者：
Jia H. [1 ,2 ]
Dong Z. [1 ,2 ]
Yan L. [1 ,2 ]
机构：
[1] Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation (North China Electric Power University), Baoding
[2] School of Control and Computer Engineering, North China Electric Power University, Beijing
来源：
| 2018年 / Science Press卷 / 39期
关键词：
Empirical wavelet transform; R-statistic test method; Steady-state detection; Trend extraction;
D O I：
10.19650/j.cnki.cjsi.J1803911
中图分类号：
学科分类号：
摘要：
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. © 2018, Science Press. All right reserved.
引用
收藏
页码：150 / 157
页数：7
相关论文
共 22 条
  • [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, Energy, 93, 12, pp. 923-944, (2015)
  • [2] Bejarano G., Rodriguez D., Alfaya J.A., Et al., On identifying steady-state parameters of an experimental mechanical-compression refrigeration plant, Applied Thermal Engineering, 109, 8, pp. 318-333, (2016)
  • [3] Talebi S.S., Tousi A.M., The effects of compressor blade roughness on the steady state performance of micro-turbines, Applied Thermal Engineering, (2016)
  • [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, IEEE Transactions on Power Electronics, 30, 11, pp. 6349-6355, (2015)
  • [5] Martinez-Maradiaga D., Bruno J.C., Coronas A., Steady-state data reconciliation for absorption refrigeration systems, Applied Thermal Engineering, 51, 1-2, pp. 1170-1180, (2013)
  • [6] Abeysekera M., Wu J., Jenkins N., Et al., Steady state analysis of gas networks with distributed injection of alternative gas, Applied Energy, 164, 6, pp. 991-1002, (2016)
  • [7] Rhinehart R.R., Automated steady and transient state identification in noisy processes, American Control Conference, pp. 4477-4493, (2013)
  • [8] Liu J.Z., Gao M., Lv Y., Et al., Overview on the steady-state detection methods of process operating data, Chinese Journal of Scientific Instrument, 34, 8, pp. 1739-1748, (2013)
  • [9] Neumann J.V., Distribution of the ratio of the mean square successive difference to the variance, Annals of Mathematical Statistics, 12, 4, pp. 367-395, (1941)
  • [10] Cao S., Rhinehart R.R., An efficient method for on-line identification of steady state, Journal of Process Control, 5, 6, pp. 363-374, (1995)
123