Joint Estimation of State and Parameter With Synchrophasors—Part II: Parameter Tracking

An approach to joint state and parameter estimation with synchrophasor data in complex situations is presented. It consists of two loosely-coupled parts: state tracking and parameter tracking. This paper focuses on the parameter tracking and the techniques dealing with the coupling. The state tracking is presented in Part I. A new prediction model for parameters with moving means is adopted. The uncertainty in the voltages is covered by pseudo measurement errors resulting in prediction-measurement-error correlation. An error-ensemble-evolution method is proposed to evaluate the correlation. An adaptive filter based on the optimal filtering with the evaluated correlation is developed, where a sliding-window method is used to detect and adapt the moving tendency of parameters. Simulations indicate that the proposed approach yields accurate parameter estimates and improves the accuracy of the state estimation, compared with existing methods.

[1]  A. S. Debs.  Estimation of Steady-State Power System Model Parameters , 1974 .

[2]  Felix F. Wu,et al.  Estimation of parameter errors from measurement residuals in state estimation (power systems) , 1992 .

[3]  W.-H. E. Liu,et al.  Parameter error identification and estimation in power system state estimation , 1995 .

[4]  S. Mokhtari,et al.  Real time recursive parameter estimation in energy management systems , 1996 .

[5]  O. Alsaç,et al.  Generalized state estimation , 1998 .

[6]  N. U. Ahmed,et al.  Linear and Nonlinear Filtering for Scientists and Engineers , 1999 .

[7]  Avinash Kumar Sinha,et al.  Dynamic state estimator using ANN based bus load prediction , 1999 .

[8]  Kazufumi Ito,et al.  Gaussian filters for nonlinear filtering problems , 2000, IEEE Trans. Autom. Control..

[9]  A. G. Expósito,et al.  Power system parameter estimation: a survey , 2000 .

[10]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[11]  Arun G. Phadke,et al.  Synchronized phasor measurements-a historical overview , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[12]  Shyh-Jier Huang,et al.  Application of sliding surface-enhanced fuzzy control for dynamic state estimation of a power system , 2003 .

[13]  P. Djurić,et al.  Particle filtering , 2003, IEEE Signal Process. Mag..

[14]  G.L. Kusic,et al.  Measurement of transmission line parameters from SCADA data , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[15]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[16]  Vesselin P. Jilkov,et al.  A survey of maneuvering target tracking: approximation techniques for nonlinear filtering , 2004, SPIE Defense + Commercial Sensing.

[17]  Shyh-Jier Huang,et al.  Enhancement of anomalous data mining in power system predicting-aided state estimation , 2004, IEEE Transactions on Power Systems.

[18]  Tianshu Bi,et al.  A novel hybrid state estimator for including synchronized phasor measurements , 2008 .

[19]  A.G. Phadke,et al.  Exploring the IEEE Standard C37.118–2005 Synchrophasors for Power Systems , 2008, IEEE Transactions on Power Delivery.

[20]  X. Rong Li,et al.  Joint Estimation of State and Parameter With Synchrophasors—Part I: State Tracking , 2011, IEEE Transactions on Power Systems.