Paper Title
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.