Identifying the unknown circuit breaker statuses in power networks

This paper describes an approach by which the circuit breaker status errors can be detected and identified in the presence of analog measurement errors. This is accomplished by using the least absolute value (LAV) state estimation method and applying the previously suggested two stage estimation approach by A. Monticelli (see ibid., vol.8, no.3, p.1143-9, 1993). The ability of the LAV estimators to reject inconsistent measurements, is exploited in order to differentiate between circuit breaker status and analog measurement errors. The first stage of estimation uses a bus level network model as in conventional LAV estimators. Results of stage 1 are used to draw a set of suspect buses whose substation configurations may be erroneous. In the second stage, the identified buses are modeled in detail using the bus sections and the circuit breaker models while keeping the bus level network models for the rest of the system. The LAV estimation is repealed for the expanded system model and any remaining significant normalized residuals are flagged as bad analog measurements, while the correct topology is determined based on the estimated flows through the modeled circuit breakers in the substations. The proposed approach is implemented and tested. Simulation results for cases involving circuit breaker status and/or analog measurement errors are provided.

[1]  G. Krumpholz,et al.  Power System State Estimation Residual Analysis: An Algorithm Using Network Topology , 1981, IEEE Transactions on Power Apparatus and Systems.

[2]  Yu Er-keng,et al.  A New Approach for Detection and Identification of Multiple Bad Data in Power System State Estimation , 1982, IEEE Transactions on Power Apparatus and Systems.

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

[4]  Ali Abur,et al.  A bad data identification method for linear programming state estimation , 1990 .

[5]  A. Monticelli The impact of modeling short circuit branches in state estimation , 1993 .

[6]  M. Ribbens-Pavella,et al.  Hypothesis Testing Identification: A New Method for Bad Data Analysis in Power System State Estimation , 1984, IEEE Power Engineering Review.

[7]  R. Lugtu,et al.  Power System State Estimation: Detection of Topological Errors , 1980, IEEE Transactions on Power Apparatus and Systems.

[8]  I. Barrodale,et al.  An Improved Algorithm for Discrete $l_1 $ Linear Approximation , 1973 .

[9]  A. Abur,et al.  A fast algorithm for the weighted least absolute value state estimation , 1991, IEEE Power Engineering Review.

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

[11]  I. S. Costa,et al.  Identification of topology errors in power system state estimation , 1993 .

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

[13]  A. Monticelli Modeling circuit breakers in weighted least squares state estimation , 1993 .

[14]  A. Abur,et al.  A fast algorithm for the weighted least absolute value state estimation (for power systems) , 1991 .

[15]  E. Handschin,et al.  Bad data analysis for power system state estimation , 1975, IEEE Transactions on Power Apparatus and Systems.

[16]  A. Monticelli,et al.  Reliable Bad Data Processing for Real-Time State Estimation , 1983, IEEE Power Engineering Review.

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