Citation :

Ahmed Saad Yahya, 2026. "Enhancing Distance Relay Performance against High Impedance Faults Using Artificial Neural Networks" ESP International Journal of Advancements in Science & Technology (ESP-IJAST)  Volume 4, Issue 2: 101-106.

Abstract :

Conventional Mho relays for distance protection face several serious limitations especially in discriminating between HIF and heavy load situations in the transmission lines. Since the current in the case of HIF is extremely limited by the fault resistance, it usually bears similarities in electrical characteristics with heavy loading scenarios and thus leading to false tripping or relay underreach with serious consequences. In this paper, an advanced and very selective distance protection as well as fault location system for 100 kilometers long transmission line has been developed using the MATLAB/Simulink software. Specifically, the key innovation lies in the design of a sophisticated two-level supervisory discrimination algorithm that considers the measured phase impedance and the residual or ground current (Ig). In the event of balanced heavy load situation, even if the measured impedance becomes less than the zone setting of the relay (106.99 ohm), the residual current does not cross the critical value of 1.65 A and therefore prevents the relay from unnecessary operations. However, in case of real HIF scenario where the non-linear electric arc dynamic is assumed to be used to model the fault occurrence, the imbalance in the structure leads to the Ig exceeding 1.65 A and thus justifying the presence of fault and eventually activating the relay through AND gate logic design. Also, an artificial neural network has been incorporated into the system that estimates the autonomous fault location by using various transient waveforms of voltages and currents along the 100 km line.

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Keywords :

Power Systems Protection, Distance Relay, Mho Characteristics, High Impedance Fault (HIF), Loaded Conditions, Artificial Neural Networks (ANN).