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Record W4409651616 · doi:10.23977/jeeem.2025.080108

Method of Harmonic Differential Protection for PV Grid-Connected Loads Based on Characteristic Signal Injection

2025· article· en· W4409651616 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2025
Typearticle
Languageen
FieldEngineering
TopicSmart Grid and Power Systems
Canadian institutionsnot available
Fundersnot available
KeywordsDifferential protectionGridSIGNAL (programming language)HarmonicDifferential (mechanical device)Harmonic analysisControl theory (sociology)Environmental scienceElectronic engineeringElectrical engineeringComputer scienceAcousticsPhysicsMathematicsEngineeringVoltageAerospace engineering

Abstract

fetched live from OpenAlex

With the rapid development of photovoltaic (PV) power generation technology, especially the widespread application of inverter-interfaced distributed generation (IIDG), its impact on distribution networks has become increasingly significant. To fully utilize the positive effects brought by IIDG integration into distribution networks, this paper proposes a method of harmonic differential protection for PV grid-connected loads based on characteristic signal injection. This method achieves effective protection of system load components by adding a characteristic signal injection strategy to the current inner-loop controller of inverters. Through theoretical analysis and simulation verification, the effectiveness of this method under different fault conditions is proven.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.208
Teacher spread0.204 · 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