Wrong transition and measurement models in power system state estimation

[1]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[2]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[3]  James V. Candy,et al.  Bayesian Signal Processing , 2009 .

[4]  K. Schneider,et al.  Feasibility studies of applying Kalman Filter techniques to power system dynamic state estimation , 2007, 2007 International Power Engineering Conference (IPEC 2007).

[5]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[6]  Dariusz Janiszewski,et al.  Particle Filter Approach for Permanent Magnet Synchronous Motor State Estimation , 2014 .

[7]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[8]  O. Cappé,et al.  Population Monte Carlo , 2004 .

[9]  Piotr Kozierski,et al.  Resampling in particle filtering : comparison , 2013 .

[10]  Gustavo Valverde,et al.  Unscented kalman filter for power system dynamic state estimation , 2011 .

[11]  Shyh-Jier Huang,et al.  Application of a Robust Algorithm for Dynamic State Estimation of a Power System , 2002 .

[12]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[13]  C. Cornell,et al.  Adaptive Importance Sampling , 1990 .

[14]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[15]  Thomas B. Schön,et al.  System identification of nonlinear state-space models , 2011, Autom..

[16]  A. G. Expósito,et al.  Power system state estimation : theory and implementation , 2004 .