Impedance Analysis of Adaptive Distance Relays Using Machine Learning

This paper introduces a novel approach to enhance the reliability of power system protection using dynamic distance relay settings. This system at first predicts Load Encroachment situations by load prediction based on machine learning (ML) or existing accurate prediction like PJM (PJM is a regional transmission organization ( www.pjm.com ) that coordinates the movement of wholesale electricity in all or parts of 13 states and the District of Columbia) predictions. The system will then recalculate distance relay settings based on new Impedance (Z = R + JX = V/I). Then when the system is back into the normal situation, settings will reset to the original settings that were used prior to the Load Encroachment situation. These calculations are done on both cloud and on-premise simultaneously which has several advantages: (1) It predicts future impedance values. (2) It monitoring the line even without a distance relay, preventing false tripping during rapid voltage changes. (3) As the proposed system recognizes Load Encroachment scenarios and employs real impedance data, erroneous relay trips are significantly reduced, enhancing overall grid stability.