Improved first zone reach setting of artificial neural network-based directional relay for protection of double circuit transmission lines

This study proposes an artificial neural network-based protection scheme for double circuit transmission line with improved first zone reach setting up to 99% of line length. The proposed scheme involves three stages. The first stage makes the discrimination among normal condition and faults. The second stage identifies the zone/section of the fault from the relay location. If a forward fault is detected in its first zone then the third stage is activated, which classifies the fault type and identifies the faulty phase. The three-phase currents and voltages measured at only one end of the double circuit line are used to calculate discreet Fourier coefficients. Thus, this technique does not require any communication link. The algorithm is presented in detail and extensively tested using the simulink model of a 400 kV, 300 km distributed parameter line simulated in MATLAB^® 2009a in the time domain. The simulation results show that all types of shunt faults (forward as well as reverse), its zone/section and faulty phase can be correctly identified within a half cycle time. This method is adaptive to the variation of fault type, fault inception angle, fault location, fault resistance, single circuit operation and CT saturation. The main advantage of the proposed scheme is that it offers primary protection to total line length using single end data only and back up protection for the adjacent forward and reverse line section also.