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Record W2538253274 · doi:10.1109/epe.2005.219193

A fixed point variable sample rate frequency adaptive bandpass filter for extraction of synchronization information from AC utility network signals

2005· article· en· W2538253274 on OpenAlexaff
Hamid Timorabadi, A. H. Abidi, F.P. Dawson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBand-pass filterAdaptive filterFinite impulse responseInfinite impulse responseComputer scienceControl theory (sociology)Filter designPrototype filterDigital filterLow-pass filterElectronic engineeringHigh-pass filterSignal processingFilter (signal processing)Digital signal processingAlgorithmEngineeringComputer hardwareArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents design procedures for two adaptive bandpass filters with variable sample rates and fixed point implementations. Each bandpass filter can be used as an integral part of a multirate phase lock loop (MPLL) for extracting the fundamental component from an input signal. The MPLL provides synchronization information from zero crossings of power system AC signals. The MPLL incorporates frequency adaptation intrinsically by generating a sample signal which is an integer multiple of the fundamental frequency component. The integer multiple frequency is used as a sampling signal for all signal processing/filtering blocks within the MPLL. The bandpass filter ensures that the MPLL is robust against disturbances that appear on the power system. Finite impulse response (FIR) and infinite impulse response (IIR) architectures are studied for the design of bandpass filters. The FIR architecture offers unconditional stability and ease of implementation and hence is selected for the MPLL. The FIR filter is implemented on a Xilinx field programmable gate array

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.217
Teacher spread0.203 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2005
Admission routes1
Has abstractyes

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