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Record W4399939030 · doi:10.1109/access.2024.3419061

The On-Line Estimation of Multi-Mode Electromechanical Oscillations Using the Cascade Structure of Damped-SOGI

2024· article· en· W4399939030 on OpenAlexaff
Mohammad Mansouri, David T. Westwick, Andrew M. Knight

Bibliographic record

VenueIEEE Access · 2024
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCascadeControl theory (sociology)Mode (computer interface)Line (geometry)PhysicsComputer scienceMathematicsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The real-time identification of low-frequency electromechanical oscillations within interconn- ected power systems is of significant interest due to its vital role in assessing the system’s stability status. These oscillations are often comprised of multiple modes, each with distinct frequencies and damping characteristics. This study introduces a measurement-based approach for determining the parameters of multi-mode electromechanical oscillations. The proposed method operates sequentially, making it ideal for real-time analysis of data gathered from Phasor Measurement Units in power systems. This approach is based on enhancing the parallel framework of an existing technique known as the Damped Second Order Generalized Integrator. The document elaborates on the newly suggested cascade configuration and offers simulation findings to validate both the theoretical analysis and the effectiveness of the proposed method. The method boasts several benefits: it supports real-time application as it processes data sequentially, exhibits resilience to noise without necessitating external pre-filtration or additional data preprocessing, can ascertain the immediate amplitude of oscillations, and is straightforward to design and execute.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.207

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 head0.047
GPT teacher head0.349
Teacher spread0.302 · 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 designSimulation or modeling
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

Citations4
Published2024
Admission routes1
Has abstractyes

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