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Record W2971306980 · doi:10.1109/tie.2019.2935930

Adaptive Time-Stepping Universal Line and Machine Models for Real Time and Faster-Than-Real-Time Hardware Emulation

2019· article· en· W2971306980 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsEmulationField-programmable gate arrayComputer scienceReal-time simulationTransient (computer programming)Hardware-in-the-loop simulationGate arrayEmbedded systemComputer hardwareSimulationReal-time computing

Abstract

fetched live from OpenAlex

Transmission lines and rotating machines that widely exist in power systems should be accurately modeled in real-time electromagnetic transient (EMT) simulation for obtaining precise results for hardware-in-the-loop applications. In the conventional EMT simulator, the time-step is fixed, which may lead to inefficiencies when the time constants of the system change. The adaptive time-stepping (ATS) method can efficaciously solve this problem; however, the ATS schemes for the universal transmission line model (ULM) and universal machine (UM) model remain to be investigated. This article derives the ATS models for ULM and UM, and the proposed ULM model is more stable than the traditional model. Both ATS models are emulated on the parallel and pipelined architecture of the field-programmable gate array (FPGA). The proposed subsystem-based ATS scheme and the local truncation error (LTE) based time-step control enable the large-scale systems to be simulated in real time and “faster-than-real-time” modes. The IEEE 39-bus system with ATS models is emulated on two interconnected FPGA boards, and the emulation results compared with PSCAD/EMTDC and fixed time-stepping (FTS) hardware emulator verify the effectiveness of the proposed models and show that the LTE of ULM and UM can be reduced by 76.5% and 62.0%, respectively, compared with the FTS simulation.

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.

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)
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 score0.540
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)0.0000.000

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 head0.021
GPT teacher head0.215
Teacher spread0.194 · 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