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Record W3171137366 · doi:10.1109/oajpe.2021.3088876

Synthetic Harmonic Distance Relaying for Inverter-Based Islanded Microgrids

2021· article· en· W3171137366 on OpenAlexafffund
Khaled A. Saleh, Mahmoud A. Allam

Bibliographic record

VenueIEEE Open Access Journal of Power and Energy · 2021
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsGovernment of CanadaNatural Resources Canada
FundersOffice of Energy Research and DevelopmentNatural Resources Canada
KeywordsReactanceMicrogridInverterHarmonicRelayFault (geology)Transient (computer programming)Computer scienceControl theory (sociology)Electronic engineeringVoltageEngineeringElectrical engineeringPower (physics)PhysicsControl (management)

Abstract

fetched live from OpenAlex

Faults in inverter-based islanded microgrids can be a formidable protection challenge due to (i) low fault current magnitudes, (ii) compromised fault current phase angles, and (iii) bidirectional flow of fault currents. This paper proposes a protection scheme that disregards the fault signals altogether. Instead, it relies on decoupled synthesized signals introduced only during fault conditions. This scheme is achieved by exploiting the existing inverter-based distributed generation (IBDG) controllers to inject synthetic harmonic voltages and currents. These synthetic signals are measured locally by digital relays in the microgrid to develop a novel synthetic harmonic distance relay (SHDR). Apart from the utilization of high-order harmonic signals that enhance SHDR reactance reach, further reach improvement is achieved via the introduction of line reactance magnifiers (LRMs). Transient studies in PSCAD/EMTDC verify the performance of the proposed scheme under various faults, contingencies, and different microgrid configurations.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.296
Teacher spread0.270 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2021
Admission routes2
Has abstractyes

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Same venueIEEE Open Access Journal of Power and EnergySame topicIslanding Detection in Power SystemsFrench-language works237,207