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Record W2141421654 · doi:10.1109/tpwrd.2004.835036

Mitigation of Voltage Disturbances Using Adaptive Perceptron-Based Control Algorithm

2005· article· en· W2141421654 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 · 2005
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVoltageHarmonicsControl theory (sociology)Disturbance voltageVoltage sagEngineeringFault (geology)AlgorithmComputer scienceVoltage regulationElectronic engineeringVoltage optimisationControl (management)Power qualityElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a generalized control algorithm for voltage disturbance extraction and mitigation. The proposed mitigating device is the dynamic voltage restorer (DVR). A DVR is commonly used to mitigate the voltage sags. In this paper the proposed DVR can compensate the voltage unbalance and mitigate voltage harmonics in the time of normal operation as well as performs its basic function during the fault condition. The suggested control algorithm employs an adaptive perceptron to effectively and adaptively track and extract the most common voltage harmonics, voltage unbalance (which include negative and zero sequence voltage drops), and different types of voltage sags, which include balanced and unbalanced voltage sags. Digital simulation results are obtained using PSCAD/EMTDC to verify the effectiveness of the proposed control algorithm. Experimental results are demonstrated to prove the practicality of the mitigating device.

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 score0.871
Threshold uncertainty score0.816

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 head0.016
GPT teacher head0.223
Teacher spread0.207 · 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