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Advanced Short Circuit Modeling, Analysis, and Protection Schemes Design for Transmission Systems under the Influence of Inverter-based Resources

2022· article· en· W4321637099 on OpenAlexaff
Mingxuan Zhao, Yuanzhu Chang, Ilhan Koçar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsOvercurrentReliability engineeringElectric power systemFault (geology)InverterComputer sciencePower-system protectionTurbineEngineeringTransmission systemTransmission (telecommunications)VoltagePower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

Inverter-Based Resources (IBRs), including Wind Turbine Generators (WTGs) and solar plants, have different and in some cases more complex fault current characteristics compared to conventional Synchronous Generators (SGs). Hence, large-scale integration of IBRs in power systems is expected to impact the performance of legacy protective relays set under the assumption of a conventional SG-dominated power system. It is therefore necessary to study the performance of protection system under IBRs to ensure its efficiency. This paper studies transmission line protection schemes of a realistic network with high concentration of offshore Wind Parks (WPs) in a wholistic manner considering line current differential, distance, and overcurrent protection schemes. Conducted fault analysis using a simulation model of the system illustrates potential protection problems in overcurrent protection and resistive coverage of distance protection under contingency scenarios. Many studies focusing on the performance of distance protection do not consider the fault resistance which increases the likelihood of maloperation under the influence of IBRs as shown in this paper. Moreover, the existing studies focus often on one protection scheme, which is not the state-of-the-art practice in high voltage transmission line protection.

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: none
Teacher disagreement score0.576
Threshold uncertainty score0.370

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 head0.026
GPT teacher head0.231
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

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

Citations2
Published2022
Admission routes1
Has abstractyes

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