Fault location in distribution networks remains challenging, especially for impedance-based techniques that typically return only an estimated distance from a reference point and thus yield multiple candidate locations on branched feeders. This paper proposes a hybrid Impedance–GA–Fuzzy method that delivers a unique fault location by coupling classical apparent-impedance calculations with a GA-tuned fuzzy inference system (FIS). First, an impedance module computes candidate fault segments and distances using measured voltages and currents. Next, a genetic algorithm automatically tunes the FIS membership functions and rule weights from simulated fault signatures across operating conditions and fault types. The FIS then disambiguates among candidates, outputs the exact line and distance to the fault, and provides a confidence index. Simulation results show that the proposed approach identifies the faulted line and location with high accuracy and low computation time, while remaining robust to load uncertainty, measurement noise, and variations in fault resistance. The results confirm the effectiveness of the hybrid scheme for fast and reliable fault location in power distribution networks.
