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Record W4389833086 · doi:10.1049/gtd2.13086

An extended impedance‐based fault location algorithm in power distribution system with distributed generation using synchrophasors

2023· article· en· W4389833086 on OpenAlex
Sandhya Chandran, Ramakrishna Gokaraju, Krish Narendra

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIET Generation Transmission & Distribution · 2023
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsPhasorFault (geology)Electrical impedanceElectric power systemUnits of measurementPower (physics)Reliability (semiconductor)VoltageEngineeringNetwork topologyFault indicatorPhasor measurement unitElectronic engineeringComputer scienceAlgorithmFault detection and isolationElectrical engineering

Abstract

fetched live from OpenAlex

Abstract Accurately locating power distribution faults reduces the total outage duration and provides better system reliability. Fault location using the traditional impedance‐based method may be very challenging in an active distribution system. However, taking into consideration the ease of implementation and cost effectiveness, a novel impedance‐based method is proposed to locate the fault by using the highly accurate time‐synchronized voltage and current phasors obtained from distribution phasor measurement units. The synchrophasor measurements obtained from the substation and various feeder segments are used in a two‐step algorithm based on the apparent impedance calculation to locate the exact source of the event. The algorithm uses phasor estimates to first identify the faulted feeder sub‐region and later uses measurements from a remote end device to eliminate pseudo‐faulted points to obtain the actual fault location. The effectiveness of the proposed method is realized using IEEE 34 bus system. Based on different fault types simulated at various parts of the system, the algorithm accurately estimates fault location in the range of ±1% of the line length. The proposed method is effective in locating faults for any type of network and topologies, with as many or as few (minimum 2) phasor measurement units in the system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.676
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.257
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it