Busbar Fault Direction Identification Using Sequences of Displacement Vectors

ABSTRACT In this paper, a novel busbar fault direction identification scheme is presented. It is based on sequences of displacement vectors. First, the proposed scheme calculates the superimposed current components. Second, it compares the values of these components to a pre‐selected threshold to detect a fault. Third, the differential elements of the superimposed components are calculated to form the sequences of displacement vectors. Fourth, the similarities of the sequences are checked using the cosine distance measure. Finally, an internal flag is activated if the fault is internal. The presented algorithm was investigated based on simulation studies of a real 400‐kV substation. The results show that the algorithm can identify evolving external‐to‐internal faults. It is also stable against severe external faults that may cause current transformers to saturate. Besides, its accuracy and speed are not affected by various fault parameters, including fault inception angle, fault resistance (up to 500 Ω), fault type, and fault location. This algorithm is fast and straightforward enough to be integrated into a differential relay to assist the percentage‐restraint differential algorithm.

Authors

Institutions

Publication Details

Journal
Engineering Reports
Published
2026-02-01
DOI
https://doi.org/10.1002/eng2.70638
Primary Topic
Power Systems Fault Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Busbar Fault Direction Identification Using Sequences of Displacement Vectors

Mostafa Sarlak
Engineering Reports
Power Systems Fault Detection
article

Busbar Fault Direction Identification Using Sequences of Displacement Vectors

Mostafa Sarlak
article en

Abstract

ABSTRACT In this paper, a novel busbar fault direction identification scheme is presented. It is based on sequences of displacement vectors. First, the proposed scheme calculates the superimposed current components. Second, it compares the values of these components to a pre‐selected threshold to detect a fault. Third, the differential elements of the superimposed components are calculated to form the sequences of displacement vectors. Fourth, the similarities of the sequences are checked using the cosine distance measure. Finally, an internal flag is activated if the fault is internal. The presented algorithm was investigated based on simulation studies of a real 400‐kV substation. The results show that the algorithm can identify evolving external‐to‐internal faults. It is also stable against severe external faults that may cause current transformers to saturate. Besides, its accuracy and speed are not affected by various fault parameters, including fault inception angle, fault resistance (up to 500 Ω), fault type, and fault location. This algorithm is fast and straightforward enough to be integrated into a differential relay to assist the percentage‐restraint differential algorithm.

Engineering ReportsVol. 8(2)
Jundi-Shapur University of Technology (IR)
Openalex Percentile: Top 50%
Power Systems Fault Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.