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Record W2787622640 · doi:10.1109/epec.2017.8286236

FPGA implementation of a phaselet method for high speed distance relaying — Preliminary results

2017· article· en· W2787622640 on OpenAlexaff
Xingxing Jin, Ramakrishna Gokaraju, Eli Pajuelo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceIEC 61850Embedded systemDigital signal processingReconfigurable computingFault (geology)Computer hardwareReal-time computingAutomationEngineering

Abstract

fetched live from OpenAlex

Fault clearing time is critical to the safety of power system equipment. Most state-of-art distance relays operate at the speed of one cycle or even longer. There are a few sub-cycle algorithms such as half-cycle type Fourier, least error square, traveling wave, and wavelet type methods. This paper utilizes a sub-cycle (phaselet) method for estimation. The algorithm and testing with IEC 61850 Sampled Value and GOOSE communication protocols was discussed in detail in the recently accepted paper by the authors in the IEEE Transactions on Smart Grids [1]. The main focus of this paper is on the hardware implementation of the phaselet method on field programmable gate arrays (FPGAs) to achieve high speed and the hardware-in-the-loop testing. The FPGA implementation of the method helps in parallelizing the algorithm and provides fast computation speed compared to sequential execution on digital signal processor (DSP). The algorithm is implemented on Xilinx Virtex 6 board. The FPGA relay is tested using hardware-in-the-loop simulations with a real time digital simulator (RTDS).

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.408

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 head0.341
Teacher spread0.320 · 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 designBench or experimental
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

Citations0
Published2017
Admission routes1
Has abstractyes

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