Imperial College London


Faculty of EngineeringDepartment of Chemical Engineering




+44 (0)20 7594 6619l.cai




ACE ExtensionSouth Kensington Campus





ABOUT ME: Dr. Lianfang Cai received his Bachelor degree in 2009 from China University of Petroleum and obtained his Ph.D. degree in 2014 from China University of Petroleum. His Ph.D. dissertation is titled as “Process fault detection and diagnosis based on independent component analysis”. He has been working as a research associate in Imperial College London since March 2015. His main research interests are in multivariate statistical analysis, data-driven process monitoring and fault detection. He is also interested in machine learning, signal processing, process control and optimization.

CURRENT SITUATION: Dr. Lianfang Cai is now conducting a postdoctoral research in Imperial College London, working on the modelling of power networks with battery energy storage systems and the detection and localization of power system disturbances. His supervisor is Prof. Nina F. Thornhill

LINKS:  [Google Scholar][RESEARCHERID]



Cai L, Thornhill NF, Kuenzel S, et al., 2018, Wide-Area Monitoring of Power Systems Using Principal Component Analysis and k-Nearest Neighbor Analysis, Ieee Transactions on Power Systems, Vol:33, ISSN:0885-8950, Pages:4913-4923

Cai L, Thornhill NF, Kuenzel S, et al., 2018, A Test Model of a Power Grid with Battery Energy Storage and Wide-Area Monitoring, Ieee Transactions on Power Systems, ISSN:0885-8950

Cai L, Thornhill NF, Pal BC, 2017, Multivariate Detection of Power System Disturbances Based on Fourth Order Moment and Singular Value Decomposition, Ieee Transactions on Power Systems, Vol:32, ISSN:0885-8950, Pages:4289-4297

Cai L, Thornhill NF, Kuenzel S, et al., 2017, Real-Time Detection of Power System Disturbances Based on k-Nearest Neighbor Analysis, Ieee Access, Vol:5, ISSN:2169-3536, Pages:5631-5639

Cai L, Tian X, Chen S, 2017, Monitoring Nonlinear and Non-Gaussian Processes Using Gaussian Mixture Model-Based Weighted Kernel Independent Component Analysis, Ieee Transactions on Neural Networks and Learning Systems, Vol:28, ISSN:2162-237X, Pages:122-135

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