Effectiveness Of Normalized Largest Residual Test Based On Bad Data Identification Process
The purpose of this paper is to implement a computational program to estimate the states of a power system and
shows the relationship between the state estimation accuracy and the effectiveness of bad data identification process. Weighted
least square (WLS) is the method chosen. In state estimation once the states are estimated the error is analyzed to detect and
identify the measurements which contain errors. Detection is done with the help of chi-square test and identification process is
done with largest normal residual test. This paper shows that on the application of largest normal residual test the bad data is
not identified for small number of iterations.
Index Terms— State Estimation, Bad Data, Chi-square Test, Largest Normalized Residual Test, Normal Residual, Tolerance.