Open AccessData‐mining‐based fault during power swing identification in power transmission system
Author(s) -
Swetapadma Aleena,
Yadav Anamika
Publication year - 2016
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2015.0169
Subject(s) - swing , tripping , fault (geology) , fault indicator , electric power transmission , power (physics) , stuck at fault , engineering , electric power system , voltage , generator (circuit theory) , control theory (sociology) , fault tree analysis , decision tree , fault detection and isolation , computer science , electronic engineering , electrical engineering , circuit breaker , reliability engineering , artificial intelligence , physics , mechanical engineering , quantum mechanics , seismology , geology , control (management) , actuator
This study proposes a decision‐tree‐based scheme for detection and classification of fault during power swing in double circuit transmission lines. The power swing may result due to switching in/out of heavy loads, switching of lines, clearance of short‐circuit faults, generator tripping or load shedding. The proposed decision tree approach makes the discrimination among no fault situation/power swing and fault during power swing. The fundamental components of currents and voltages and zero sequence currents measured at only one end of the double circuit line are used as input to decision tree. To ascertain validity of the proposed scheme, it is tested for variation in fault type, fault inception angle, fault location and fault resistance. The main advantage of this proposed scheme is that it detects fault during power swing within half cycle time and classify the type of fault and identify the faulty phase also.