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Multi-FPGA Solution for Large Power Systems and Microgrids Real Time Simulation

2019· article· en· W3004674047 on OpenAlexaff
Amine Yamane, TejKiran Rangineed, Luc-André Grégoire, Syed Qaseem Ali, Jean‐Nicolas Paquin, Jean Bélanger

Bibliographic record

Venue2019 IEEE Conference on Power Electronics and Renewable Energy (CPERE) · 2019
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsField-programmable gate arrayMicrogridComputer scienceFlexibility (engineering)Electric power systemTransient (computer programming)Hardware-in-the-loop simulationEmbedded systemReal-time simulationPower (physics)Interface (matter)Co-simulationControl (management)Operating system

Abstract

fetched live from OpenAlex

This paper presents a Multi FPGA based solution for large power systems and Microgrids real-time simulation. The proposed platform allows control and protection devices to be designed and tested in virtual power system, before they can be implemented in a physical system. This solution promotes flexibility of operation with no risk of components failure under any contingency analysis. To demonstrate the effectiveness of the OPAL-RT platform, a large distribution network (DN) of 210 bus bars and a Microgrid (MG) are considered. The MG includes a solar panel and a battery energy storage system (BESS). The complete power system including the DN and MG is simulated using only two Kintex-7 FPGA boards. The controllers of the MG distributed energy resources (DER) are compiled and real time simulated using a 3.5 GHz Intel processor. The performance of this overall Processor-In-The-Loop application (PIL) is validated based on two criteria: 1) Evaluation of the results accuracy compared to the reference offline simulation under steady state and transient conditions. 2) Evaluation of the losses introduced by the power interface used to split the DN into two FPGA boards.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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