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.
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页码：150 / 157
页数：7
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