Dynamic data reconciliation based on robust estimation and particle filters
Since process measurement data inevitably contain random or gross errors, it requires reconciliation. Dynamic data reconciliation methods have limitations in handling. Based on the robust estimation principle, a new robust estimation function is proposed in this paper. With its advantages in mathematic concepts and parameter adjustment, it can reconcile and detect random and gross errors simultaneously. The dynamic data correction method for particle filter has the problem of particle shortage. The combination of robust estimation and particle filter is used to update the particle weight by means of a robust function. Moreover, the particle filter can increase the particle confidence and avoid the degradation of particles in the dynamic processes. The efficiency of the proposed approach is proved by the results of simulation performed on CSTR system.
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Yong-gen Yuan, Huasheng Li. Process System Measurement Data Reconciliation Technology [M]. China Petrochemical Press, 1996.
Li Tong. Research on Nonlinear Dynamic Data Correction [J]. Computer & Applied Chemistry, 2009 ,25 (4):483-485.
Mingfang Kong, Bingzhen Chen. Robust Estimation Synchronization Method of Data Rectification and Error Detection [J]. Journal of Tsinghua University: Natural Science, 2000, 40(2):46-50.
Qian Gao. Robust data reconciliation theory and application [D]. Shanghai Jiao Tong University, 2007.
Shiqiang Hu, Zhongliang Jing. Particle filter principle and application [M]. Science Press, 2010.
Chunyang Jiang, Tong Qiu, Bingzhen Chen, et al. An improved dynamic data rectification method based on robust estimation [J]. Computers & Applied Chemistry, 2007, 24(10):1297-1301
Mingfang K., Bingzhen C., Bo L. An integral approach to dynamic data rectification [J]. Computers & Chemical Engineering, 2000, 24(2):749-753.
Guojing Yin, Guohai Liu, Congli Mei, et al. Dynamic data rectification based on particle filter and process model [I]. Computers & Applied Chemistry, 2011, 28 (7):911-914.
Jing Zhang, Guohai Liu, Congli Mei , Lihao Yan. Research on Dynamic Data Correction Based on Improved Particle Swarm Optimization . Energy Research and Information, 2013, 29(03):167-172.
Lin Gao. Research and Application of Data Reconciliation Technology [D]. East China University of Science and Technology, 2010.
Xiaofei Lu, Peng Fu, Ming Zhuang, Lilong Qiu, Liangbing Hu. Process Modeling and Dynamic Simulation for EAST Helium Refrigerator [I]. Plasma Science and Technology, 2016, 18(06):693-698