A Recursive Bayesian Approach for Identification of Network Configuration Changes in Distribution System State Estimation

This paper deals with the identification of network configuration changes in distribution system state estimation (DSSE). The models of critical network configurations are stored in the form of a model bank. A recursive Bayesian approach which utilizes the output of the state estimation function of each model in the bank is used to identify the correct configuration of the network. The concept is demonstrated on a part of the U.K. Generic Distribution System (UKGDS) model.

[1]  Grantham K. H. Pang,et al.  Solving data acquisition and processing problems in power systems using a pattern analysis approach , 1991 .

[2]  Felix F. Wu,et al.  Detection of topology errors by state estimation (power systems) , 1989 .

[3]  D. N. Ewart.  Power: Whys and wherefores of power system blackouts: An examination of the factors that increase the likelihood and the frequency of system failure , 1978, IEEE Spectrum.

[4]  Felix F. Wu,et al.  Detection of Topology Errors by State Estimation , 1989, IEEE Power Engineering Review.

[5]  G.J. Anders,et al.  Reliability considerations in accelerated life testing of electrical insulation with generalized life distribution function , 1991 .

[6]  Fernando L. Alvarado,et al.  Network topology determination using least absolute value state estimation , 1995 .

[7]  K. Clements,et al.  Detection and identification of topology errors in electric power systems , 1988 .

[8]  Yunxin Zhao,et al.  Online Bayesian tree-structured transformation of HMMs with optimal model selection for speaker adaptation , 2001, IEEE Trans. Speech Audio Process..

[9]  Jorma Rissanen.  Optimal Estimation , 2011, ALT.

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

[11]  S. C. Srivastava,et al.  Topology processing and static state estimation using artificial neural networks , 1996 .

[12]  H. Glavitsch,et al.  Detection and identification of topological errors in online power system analysis , 1991 .

[13]  M. R. Irving,et al.  Substation data validation , 1982 .

[14]  George J. Anders,et al.  Probability Concepts in Electric Power Systems , 1990 .

[15]  R. Vinter,et al.  Measurement Placement in Distribution System State Estimation , 2009, IEEE Transactions on Power Systems.

[16]  Ramesh R. Rao,et al.  Experimental studies on multiple-model predictive control for automated regulation of hemodynamic variables , 2003, IEEE Transactions on Biomedical Engineering.