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Closed-Loop Traveling-Wave Relay Testing (TWRT) using RTDS Real-Time Simulators

2019· article· en· W3093891672 on OpenAlexaff
Ramin Mirzahosseini, Yue Chen, Yi Zhang, R. Kuffel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsRTDS Technologies (Canada)
Fundersnot available
KeywordsReal Time Digital SimulatorTransmission lineRelayElectric power transmissionComputer scienceSimulationField-programmable gate arrayReal-time simulationLine (geometry)Power (physics)Electronic engineeringElectric power systemPower system simulationHardware-in-the-loop simulationTransmission (telecommunications)EngineeringElectrical engineeringEmbedded systemTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a methodology to perform closed-loop Traveling Wave Relay Testing (TWRT) for transmission lines in power systems using RTDS real-time simulator. The Traveling-Wave Relays (TWRs) have a high sampling rate, e.g., around 1MHz, therefore, one requirement for TWRT is that the simulator operates at a small time-step, i.e., in the order of one microsecond. The TW-based protection elements operate based on current and voltage TW signals that are derived from the High-Frequency (HF) response induced by faults on the transmission line. This paper shows that employing inaccurate line models in a simulator can result in incorrect TW signals. Therefore, another requirement for TWRT is to use accurate line models. Fulfilling these requirements requires high computational power and introduces challenges to real-time simulators for the TWRT application. This paper presents two real-time simulator approaches for TWRT: (i) an FPGA-based and (ii) a multi-core CPU-based approach. The proposed approaches are employed to test commercially available TWRs and the results are reported.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
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)0.0000.001

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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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