<![CDATA[Recent power system is structurally difficult and is vulnerable to undesirable conditions like transmission faults. In this event of transmission line faults, exact fault zone detection enhances the restoration process, thus improving reliability of the complete power system. In order to solve the above problem, this paper presents an adaptive neuro-fuzzy inference system (ANFIS) based fault zone detector, which combines artificial neural network (ANN) and fuzzy logic technique (FLT) in six phase overhead transmission lines (SPOTL). To overcome the limitation of ANN and fuzzy expert system (FES) architectures and, the selection work has been formulated as an optimization method and solved using ANFIS. The inputs are the zero sequence component currents at the middle bus of the transmission line. The training data are extracted using discrete Fourier transform and collected, and then ANFIS is trained to identify the fault zone. The ANFIS based scheme reach setting has been checked for various types of faults, with a wide range of faults and transmission line parameters. Simulation study ensure that this method has a high reach setting, does not require the design of communication channel. Further, the ANFIS study shows that ANFIS is suitable for all type of faults. The ANFIS significantly outperforms other techniques proposed in the literature in terms of various evaluation metrics.]]>
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