Double Line Fault Classification Using ANN

ELECTRICAL

The protection of double circuit transmission lines could be a difficult task. This paper presents a protection technique supported the high-frequency transients generated by the fault to hide nearly the entire length of double circuit transmission lines. For this purpose, befittingly designed line traps area unit put in at terminals of the protected line, and therefore the Artificial Neural Network with appropriate range of Neurons is employed to classify the faults supported the frequency spectrum of the RMS current and RMS voltage signals. In depth simulation studies indicate that the projected approach is well capable of discriminating differing types of faults and provides a really quick, secure, and reliable protection technique The simulation model done in MATLAB simulink for system analysis. In this model 300 km, 25 KV, 50Hz transmission line power system model design with three zone bus bar system. One end bus bar data measurement utilized for three phase RMS voltage and current measurement. Also Neural Network training done for designed power system model using MATLAB Simulink.

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