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Record W2137994360 · doi:10.1109/imtc.2005.1604402

A Filtering Technique for Three-Phase Power Systems

2006· article· en· W2137994360 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

Venue2005 IEEE Instrumentationand Measurement Technology Conference Proceedings · 2006
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsQueen's UniversityYork UniversityUniversity of Toronto
Fundersnot available
KeywordsRoot-raised-cosine filterAdaptive filterControl theory (sociology)Kernel adaptive filterFilter designFilter (signal processing)Computer scienceRobustness (evolution)All-pass filterLow-pass filterAnti-aliasing filterAliasingHigh-pass filterMathematicsAlgorithmArtificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

A novel filter for use in three-phase power systems is introduced. When the input to the filter is a three-phase balanced set of signals, the filter suppresses noise and distortions and extracts a smooth version of the fundamental components. When the input signal to the filter is unbalanced, it extracts the positive-sequence components of the input signal. The filter also estimates the magnitude, phase-angle and frequency of the input signal while adaptively accommodates variations in all these three variables. Characteristics of the filter including its mathematical equations as well as steady-state and dynamic responses are discussed in this paper. Structural simplicity and robustness of the filter make it desirable for power system application. It can specifically be used as an adaptive anti-aliasing filter

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score1.000

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 head0.019
GPT teacher head0.223
Teacher spread0.204 · 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