Artificial neural network approach to distance protection of transmission lines

TLDR

Distance relays for transmission lines are typically designed with fixed settings, making their reach sensitive to changing network conditions. This study aims to advance protection by implementing a pattern recognizer for power system diagnosis. An artificial neural network uses the magnitudes of three‑phase voltage and current phasors as inputs to classify distance relay operation. The ANN approach improves performance by maintaining correct relay reach across varying fault conditions and network configurations.

Abstract

A distance relay for the protection of transmission lines is usually designed on the basis of fixed settings. The reach of such relays is therefore affected by the changing network conditions. The implementation of a pattern recognizer for power system diagnosis can provide great advances in the protection field. This paper demonstrates the use of an artificial neural network as a pattern classifier for a distance relay operation. The scheme utilizes the magnitudes of three phase voltage and current phasors as inputs. An improved performance with the use of an artificial neural network approach is experienced once the relay can operate correctly, keeping the reach when faced with different fault conditions as well as network configuration changes.