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An Effective Zone-3 Supervision of Distance Relay for Enhancing Wide Area Back-Up Protection of Transmission System

Distance relays are quite vulnerable to maloperation during dynamic stressed situations. The apparent impedance seen by the relay encroaches the zone-3 resulting in distance relay maloperation that can initiate cascading failures. This paper presents an enhanced scheme for wide area backup protection which implements improved protection algorithms to decide final relaying status. A machine learning tool namely Deep Neural Network is utilized to build those protective relaying decision logics: RDL-1 and RDL-2. Wide area information retrieved from phasor measurement units (PMU) is used to take real time decisions. RDL-1 is designed to detect if the system is operating in normal or stressed condition. During normal state, traditional zone-3 relay undertakes the relaying decision. However, under stressed situations, RDL-1 switches the decision-taking to RDL-2. RDL-1 also predicts whether the system is progressing towards an out-of-step situation so that appropriate remedial action can be undertaken. Proposed scheme was studied on IEEE-39 bus New England system using MATLAB/SIMULINK and validated on Real Time Digital Simulator platform and third zone mal-tripping are avoided. Performance of the proposed algorithm was compared with the conventional blinder and other data-mining techniques which show considerably high classification accuracy.