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Record W569086156

Unified knowledge model for stability analysis in cyber physical systems

2012· article· en· W569086156 on OpenAlexaboutno aff
Tamal Paul

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsCyber-physical systemCorrectnessComputer sciencePhysical systemStability (learning theory)Voltage droopLyapunov functionInvariant (physics)GridDistributed computingControl engineeringEngineeringMathematicsProgramming languageElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

The amalgamation and coordination between computational processes and physical components represent the very basis of cyber-physical systems. A diverse range of CPS challenges had been addressed through numerous workshops and conferences over the past decade. Finding a common semantic among these diverse components which promotes system synthesis, verification and monitoring is a significant challenge in the cyber-physical research domain. Computational correctness, network timing and frequency response are system aspects that conspire to impede design, verification and monitoring. The objective of cyber-physical research is to unify these diverse aspects by developing common semantics that span each aspect of a CPS. The work of this thesis revolves around the design of a typical smart grid-type system with three PV sources built with PSCAD R ©1. A major amount of effort in this thesis had been focused on studying the system behavior in terms of stability when subjected to load fluctuations from the PV side. The stability had been primarily reflected in the frequency of the generator of the system. The concept of droop control had been analyzed and the parameterization of the droop constant in the shape of an invariant forms an essential part of the thesis as it predicts system behavior and also guides the system within its stable restraints. As an extension of a relationship between stability and frequency, the present study goes one step ahead in describing the sojourn of the system from stability to instability by doing an analysis with the help of tools called Lyapunov-like functions. Lyapunov-like functions are, for switched systems, a class of functions that are used to measure the stability for non linear systems. The use of Lyapunov-like functions to judge the stability of this system had been tested and discussed in detail in this thesis and simulation results provided. PSCAD R © is a registered trademark of Manitoba HVDC Research Center Inc.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.282

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 head0.030
GPT teacher head0.263
Teacher spread0.233 · 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 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

Citations2
Published2012
Admission routes1
Has abstractyes

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