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A High Speed Method for Loss of Excitation Detection

2022· article· en· W4309227274 on OpenAlexaff
Soumesh Chatterjee, Dhrubajyoti Das, Kuntal Bhattacharjee

Bibliographic record

Venue2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT) · 2022
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsGenerator (circuit theory)Robustness (evolution)Control theory (sociology)ExcitationShunt generatorArmature (electrical engineering)Computer scienceElectric power systemPermanent magnet synchronous generatorSwingAC powerElectric generatorElectrical impedancePower (physics)EngineeringElectrical engineeringVoltageElectromagnetic coilPhysicsControl (management)

Abstract

fetched live from OpenAlex

In case of generator protection, accurate detection of loss of excitation (LOE) of generator is a major concern for power system operation. The conventional approach based on impedance trajectory is prone to mal-operate during stable power swing (SPS) and any large disturbance near the generator bus. Any mal-operation of generator relays threatens both the generator and power system stability. This paper presents a new approach only by investigating the armature current and active power at the generator terminal. This method doesn't consider any intentional time delay. Robustness of method is assured considering different types of loadings. In this work, both the total loss of excitation (TLOE) and partial loss of excitation (PLOE) have been considered. The proposed method is independent of the generator size. The verification of the proposed method and all the required simulations have been done in PSCAD platform.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.278
Teacher spread0.261 · 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
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

Citations2
Published2022
Admission routes1
Has abstractyes

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