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An Active Protection Scheme for Microgrids Based on the Harmonic Injection

2021· article· en· W4206469122 on OpenAlexaboutno aff
Aiming Xia, Yang Chen, Bin Fu, Fengyu Wu, Yuchen Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsnot available
Fundersnot available
KeywordsMicrogridFault (geology)HarmonicInverterRelayControl theory (sociology)EngineeringElectronic engineeringDistributed generationElectrical impedanceEnergy (signal processing)Computer scienceVoltageElectrical engineeringPower (physics)Renewable energyMathematicsPhysicsAcoustics

Abstract

fetched live from OpenAlex

Inverter Interfaced Distributed Generation brings major challenges to the protection of microgrids, the traditional passive fault observation that does not use IIDG controllability, and the protection scheme based on the premise that the fault and non-fault characteristics are significantly different are no longer applicable. This paper proposes an active protection scheme for microgrid based on harmonic energy injection. The IIDG interface electrical signals are used for fault detection. In case of fault, the IIDG inverter started the harmonic energy injection function based on equivalent impedance modulation. The adjacent IIDG injected different frequencies’ harmonic energy. The closer the fault point was to IIDG, the greater the harmonic energy injected. The scheme collects the fault harmonic current in the relay, and sets the protection criterion based on the ratio of the detected harmonic currents of different IIDGs. The minimum range of fault clearing is realized by blocking logic. A harmonic current ratio relay is designed for the protection scheme. The proposed active protection scheme was suitable for microgrids with multiple IIDGs, and can meet both connected and island operation mode. A benchmark radial system practically under use in Canada was established in PSCAD/EMTDC, and the effectiveness of the scheme under different fault conditions was verified through simulation research.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.670
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.018
GPT teacher head0.236
Teacher spread0.218 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2021
Admission routes1
Has abstractyes

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