MétaCan
Menu
Back to cohort
Record W2570398923 · doi:10.1109/tpwrd.2017.2650480

A Digital Frequency Adaptive Synchronization Unit for On- and Off-Grid Systems

2017· article· en· W2570398923 on OpenAlexaff
Essam S. Elsahwi, Adrian Z. Amanci, F.P. Dawson

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)Adaptive filterPhase distortionElectronic engineeringEngineeringComputer scienceFinite impulse responseFilter (signal processing)Electrical engineering

Abstract

fetched live from OpenAlex

This paper presents the analysis and implementation details of a frequency adaptive synchronization unit (FASU) capable of extracting the positive, negative, and zero sequence components of a three-phase signal as well as real-time tracking of the amplitude, phase, and frequency of the positive sequence component. The FASU consists of three subsystems: First, a sequence component extractor block based on a fast retrieval technique, second, a zero-crossing peak detection system with dynamic dc compensation and, third, a frequency adaptive multi-input multioutput low-pass finite-impulse response filter. The frequency adaptive filter is able to provide filtering of the positive sequence component over a wide frequency range (40-2000 Hz), in the presence of significant input signal distortion (total harmonic distortion as high as 100%). The system achieves a worst case transient response time of one and a half-cycle of the input period, in the event of input transients such as balanced/unbalanced amplitude sags and swells, balanced/unbalanced phase steps, and positive/negative frequency ramps (up to 700 Hz/s). The proposed system is suitable for use in islanded microgrids, in the aerospace industry, and as a phasor measurement unit. The system is implemented as a proof of concept on a field programmable gate array hardware platform.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.647

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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Power DeliverySame topicMicrogrid Control and OptimizationFrench-language works237,207