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Record W2220323444 · doi:10.1109/ias.2015.7356953

Developing and testing phaselet frames-based digital protection for distributed generation units

2015· article· en· W2220323444 on OpenAlexaff
S. A. Saleh, E. Ozkop, A. S. Aljankawey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsConcordia University
Fundersnot available
KeywordsFault (geology)Transient (computer programming)Fault detection and isolationComputer scienceDigital filterFilter (signal processing)Electronic engineeringCoupling (piping)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents the development and performance testing of a new digital protection for interconnected distributed generation units (DGUs). The developed digital protection is structured to detect and respond to transient disturbances (fault and non-fault conditions) based on the magnitudes and phases of the high frequency sub-band contents extracted from the d-q axis components of the currents flowing through the point-of-common-coupling (PCC). These magnitudes and phases are extracted by employing a set of 6 phaselet frames. The employed phaselet frames are realized by a modulated filter bank that is composed of 6 digital high pass filters (HPFs). The coefficients of digital HPFs are determined by bi-orthogonal phaselet basis functions. Extracted magnitudes and phases of the high frequency sub-band contents of the PCC d-q axis current components provide signature information for accurate detection and identification of faults. The performance of phaselet frames-based digital protection is experimentally tested for two wind energy conversion systems and a photovoltaic system under different fault and non-fault conditions. Test results of the proposed digital protection demonstrate reliable and timely responses, along with negligible sensitivity to the type and control of DGUs, type and location of faults, and loading levels.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score0.401

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 head0.112
GPT teacher head0.264
Teacher spread0.153 · 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 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

Citations5
Published2015
Admission routes1
Has abstractyes

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