Detect and categorize errors in smart grids using voltage and current phases

This paper presents a methodology for detecting and classifying the errors occurring on smart power transmission lines. In the proposed method, the voltage and current phases are estimated by the phasor measurement unit (PMU) installed in the generator bus, and then the equivalent voltage and current angles are obtained. These angles are analyzed by fast fourier transform (FFT) and used to detect of transmission line errors. Detection of the transmission line error is performed using the nerve- fuzzy inference system methodology, and the diagnostic error classification is performed using support vector machine (SVM). Validation of the proposed method for the IEEE 14 system is also tested in the MATLAB software environment.