基于改进收敛交叉映射的厂级振荡源诊断

doi:10.3969/j.issn.1672-6952.2021.05.015

辽宁石油化工大学学报 ›› 2021, Vol. 41 ›› Issue (5): 85-90.DOI: 10.3969/j.issn.1672-6952.2021.05.015

基于改进收敛交叉映射的厂级振荡源诊断

周学均¹(), 陈小强², 谢磊³

^1.中电国际胡布发电有限公司, 巴基斯坦卡拉奇 74200
^2.中电华创电力技术研究有限公司，江苏苏州 215123
^3.浙江大学智能系统与控制研究所，浙江杭州 310027

收稿日期:2021-06-11 修回日期:2021-09-01 出版日期:2021-10-25 发布日期:2021-11-09
作者简介:周学均（1986⁃），男，工程师，从事大型火电热控方面的研究；E⁃mail:zhouxuejun_zdhc@163.com。
基金资助:
国家自然科学基金项目(62073286)

Location of Plant⁃Level Oscillation Source Based on Improved Convergent Cross Mapping

Xuejun Zhou¹(), Xiaoqiang Chen², Lei Xie³

^1.China Power Hub Generation Company，Karachi 74200，Pakistan
^2.CLP Huachuang Power Technology Research Co. ，Ltd. ，Suzhou Jiangsu 215123，China
^3.Institute of Cyber?Systems and Control，Zhejiang University，Hangzhou Zhejiang 310027，China

Received:2021-06-11 Revised:2021-09-01 Published:2021-10-25 Online:2021-11-09

摘要/Abstract

摘要：

由于噪声、周期性、非线性和非平稳性的干扰，现有的大多数因果分析方法在工业过程控制系统的应用中往往是不可靠和不准确的。为提高厂级振荡源定位的性能，提出了一种基于改进收敛交叉映射的因果关系检测方法。首先，指出噪声和周期性对因果关系检测的不利影响。其次，将经验模态分解和去趋势波动分析相结合，实现了振荡信号去噪。然后，通过奇异谱分析有效地去除信号的周期性。利用收敛交叉映射可以对去噪去周期后的信号进行分析，进而准确地定位厂级振荡的源头。仿真结果表明，所提方法能提高过程控制系统中厂级振荡源定位的准确性。

关键词: 振荡检测, 因果分析, 收敛交叉映射, 格兰杰因果, 故障诊断, 过程控制系统性能评估

Abstract:

Due to the interference of noise, periodicity, nonlinearity and nonstationarity, most of the existing causal analysis methods are often unreliable and inaccurate in industrial process control systems. In order to improve the plant-level oscillation source location performance, a causal relationship detector based on improved convergence cross mapping was proposed. First, the adverse effects of noise and periodicity on causality detection were pointed out. Then, the empirical mode decomposition (EMD) and the trend fluctuation analysis were combined to realize the oscillation signal denoising. The periodicity of the signal was effectively removed by singular spectrum analysis. The denoising and periodicity?removing signal was analyzed by using convergent cross mapping, and then the source of plant level oscillation could be accurately located. Simulation results show that, the proposed method can improve the accuracy of plant?level oscillation source location in process control system.

Key words: Oscillation detection, Causality analysis, Convergent cross map, Granger causality, Fault diagnosis, Control performance assessment

中图分类号:

TE968

周学均, 陈小强, 谢磊. 基于改进收敛交叉映射的厂级振荡源诊断[J]. 辽宁石油化工大学学报, 2021, 41(5): 85-90.

Xuejun Zhou, Xiaoqiang Chen, Lei Xie. Location of Plant⁃Level Oscillation Source Based on Improved Convergent Cross Mapping[J]. Journal of Liaoning Petrochemical University, 2021, 41(5): 85-90.

图/表 5

图1 基本CCM和迟延CCM的因果分析效果

图2 CCM的周期和噪声影响结果

图3 去噪去周期的CCM流程

图4 去噪去周期后的CCM的结果

图 5 基于改进CCM的因果流图

参考文献 13

1	Dambros J， Trierweiler J O， Farenzena M. Oscillation detection in process industries–part I： Review of the detection methods［J］. Journal of Process Control， 2019， 78： 108⁃123.
2	Chen Q M， Lang X， Xie L， et al. Detecting nonlinear oscillations in process control loop based on an improved VMD［C］// 2009 IEEE 12th International Conference on Computer Vision，［S.n.］：IEEE：91446⁃91462.
3	Chen Q M， Chem J， Lang X， et al. Detection and diagnosis of oscillations in process control by fast adaptive chirp mode decomposition［J］. Control Engineering Practice， 2020， 97（12）： 104307⁃104321.
4	Bauer M， Thornhill N F. A practical method for identifying the propagation path of plant⁃wide disturbances［J］. Journal of Process Control， 2008， 18（7⁃8）： 707⁃719.
5	Yuan T, Qin S J. Root cause diagnosis of plant⁃wide oscillations using Granger causality[J]. IFAC Proceedings Volumes, 2012, 45(15):160⁃165.
6	Zhang L， Zheng J， Xia C. Propagation analysis of plant⁃wide oscillations using partial directed coherence［J］. Journal of Chemical Engineering of Japan， 2015， 48（9）： 766⁃769.
7	Yang F， Duan P， Shah S L， et al. Capturing connectivity and causality in complex industrial processes［M］. Berlin： Springer Science & Business Media， 2014： 2⁃35.
8	Linder B， Auret L， Bauer M， et al. Comparative analysis of granger causality and transfer entropy to present a decision flow for the application of oscillation diagnosis［J］. Journal of Process Control， 2019， 79（2）：72⁃84.
9	Ye H， Deyle E R， Gilarry L J， et al. Distinguishing time⁃delayed causal interactions using convergent cross mapping［J］. Scientific Reports， 2015，5： 514750.
10	Huang N E， Shen Z， Long S R， et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non⁃stationary time series analysis［J］. Proceedings of the Royal Society of London Series A： Mathematical， Physical and Engineering Sciences， 1998， 454（1971）： 903⁃995.
11	Kantelhardt J W， Zschiegner S A， Koscielny⁃Bunde E， et al. Multifractal detrended fluctuation analysis of nonstationary time series［J］. Physica A： Statistical Mechanics and Its Applications， 2002， 316（1⁃4）： 87⁃114.
12	Hassani H， Thomakos D. A review on singular spectrum analysis for economic and financial time series［J］. Statistics and Its Interface， 2010， 3： 377⁃397.
13	Xie T, Wang J G, Xie Z T, et al. Root cause diagnosis of plant⁃wide oscillation using spectral granger causality based on phase resampling test[C]// 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)，［S.n.］：IEEE：1230⁃1235

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基于改进收敛交叉映射的厂级振荡源诊断

Location of Plant⁃Level Oscillation Source Based on Improved Convergent Cross Mapping

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