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Record W2112233368 · doi:10.1109/tpwrd.2004.829110

A Processing Unit for Symmetrical Components and Harmonics Estimation Based on a New Adaptive Linear Combiner Structure

2004· article· en· W2112233368 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2004
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHarmonicsWaveformHarmonicComponent (thermodynamics)Electronic engineeringElectric power systemHarmonic analysisSymmetrical componentsTopology (electrical circuits)Control theory (sociology)Power (physics)VoltageComputer scienceEngineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Symmetrical components as well as harmonic tracking are of great importance in many applications in power systems such as power quality and protection. This paper introduces a novel Adaptive linear combiner (ADALINE) structure for symmetrical components estimation. This structure is capable of dealing with multi-output systems for parameter tracking/estimation rather than the existing ADALINE, which deals only with single output systems. As the new topology deals with Multi-Output systems, it is called MO-ADALINE. Moreover, the paper presents a new processing unit, which can estimate symmetrical and harmonic components from the measured current signals. The advantages of this proposed unit are its independence of the voltage waveform and its ability to give information about the reactive component of the resolved current. Simulation results are given to validate the proposed algorithms.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.859
Threshold uncertainty score0.953

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 head0.039
GPT teacher head0.252
Teacher spread0.212 · 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