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Record W2771782323 · doi:10.1109/iecon.2017.8216454

Stability study of power hardware in the loop (PHIL)simulations with a real solar inverter

2017· article· en· W2771782323 on OpenAlexaff
Mandip Pokharel, Carl Ngai Man Ho

Bibliographic record

VenueIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society · 2017
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHardware-in-the-loop simulationStability (learning theory)InverterLoop (graph theory)Power (physics)Computer sciencePower inverterPhotovoltaic systemControl theory (sociology)Electrical engineeringPhysicsEngineeringEmbedded systemMathematicsArtificial intelligenceControl (management)Thermodynamics

Abstract

fetched live from OpenAlex

The Stability issues in PHIL simulations has always been of major concern due to the inherent delays within the interface devices. Various control algorithms are proposed in literature to overcome this problem. The paper uses a simple filtering technique followed by compensation blocks to overcome the stability problem. This paper further proposes a mathematical approach to consider the stability problem. This approach can be further investigated to design the interface devices that can guarantee the system stability. The stable operation of PHIL system with linear and non-linear devices was analyzed and experimentally verified. A 140W grid connected Solar inverter in a PHIL showed a stable operation even during transients.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.060
GPT teacher head0.266
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2017
Admission routes1
Has abstractyes

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