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A Study on improvement of single phase PLL algorithm stability and accuracy

2025· article· en· W4413145144 on OpenAlexaff
In Kwon Park, Yi Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsRTDS Technologies (Canada)
Fundersnot available
KeywordsPhase-locked loopStability (learning theory)Computer scienceAlgorithmPhase (matter)Control theory (sociology)JitterArtificial intelligenceTelecommunicationsPhysicsMachine learning

Abstract

fetched live from OpenAlex

A distinguishing characteristics of distribution systems is their inherent imbalance. While the assumption of three-phase balance is fundamental to most transmission-level analyses and theoretical studies-and is generally valid in practical applications-distribution systems seldom operate under such ideal conditions. In many instances, a single feeder line extends from the distribution substation. Even when a feeder is designed as a three-phase system, the connected loads often draw power from only one or two phases rather than all three. Consequently, the measured system voltages and currents do not form a balanced three-phase set. When a power electronic device interfaces with an AC power system, it must accurately track the system’s frequency and phase angle. A widely adopted approach for this purpose is the Phase-Locked Loop (PLL). Among various PLL implementations, those based on orthogonal transformations, such as the DQ transformation, are particularly prevalent. This paper presents a single-phase PLL, a variant of the DQ transformation-based PLL, which utilizes a pseudo-quadrature-axis signal generated through a simple phase delay. Additionally, an interpolation technique is incorporated to enhance tracking accuracy. The theoretical foundation of this method is outlined, followed by a comparative evaluation demonstrating the proposed PLL’s effectiveness relative to existing techniques.

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 score0.832
Threshold uncertainty score0.204

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 head0.032
GPT teacher head0.311
Teacher spread0.279 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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