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Record W2538995973 · doi:10.1109/iecon.2008.4757994

A high-performance controllable AC load

2008· article· en· W2538995973 on OpenAlexaff
Mehrdad Kazerani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPower factorAC powerConstant currentRectifier (neural networks)Constant power circuitPower (physics)CapacitorActive loadElectrical impedanceControl theory (sociology)Electrical engineeringElectronic engineeringEngineeringComputer scienceResistorCurrent (fluid)Voltage

Abstract

fetched live from OpenAlex

Controllable AC loads are used to test AC sources, inverters and uninterruptible power supplies. They are designed to simulate constant-current, constant-resistance, constant-power, variable power factor, and nonlinear loads. They are also used to emulate variable AC load profiles in test-bench study of standalone energy conversion systems in research labs. High speed of response to the electronic control signal, high fidelity, high power density and low cost are among the desired characteristics of such loads. In this paper, a high-performance controllable AC load featuring simple structure, scalability, fast response and low cost, is introduced. The load can be configured to operate in single- or three-phase mode. It can be programmed to operate in controlled-current, impedance, active power, active and reactive power, as well as active power and power factor modes. The load can also emulate controlled-current diode-rectifier loads with a dc-side filter capacitor. The proposed controllable AC load can be easily configured as a controllable DC load, resulting in a compact controllable AC/DC load. The analytical expectations about the performance of the proposed controllable AC load have been verified through simulation.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.870

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)0.0000.001

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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 designBench or experimental
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

Citations19
Published2008
Admission routes1
Has abstractyes

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