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Record W4392371079 · doi:10.18280/jesa.570130

Identifying Key Reliability Factors in Micro-Grid Systems Using Principal Component Analysis

2024· article· fr· W4392371079 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2024
Typearticle
Languagefr
FieldEngineering
TopicSmart Grid and Power Systems
Canadian institutionsnot available
FundersCovenant University
KeywordsPrincipal component analysisKey (lock)Reliability (semiconductor)Reliability engineeringComponent (thermodynamics)Computer scienceGridEngineeringArtificial intelligenceMathematicsComputer security

Abstract

fetched live from OpenAlex

One way to solve the shortage in power supply and the rapid load growth is by operating power systems that could improve power supply reliability.The study aims to carry out a holistic evaluation by identifying the several reliability variables that could influence the micro-grid power system's reliability which is vital in electricity generation.Thirty-three reliability variable factors that are commonly observed to influence power systems reliability were chosen for the micro-grid power systems study and examined using the principal component analysis (PCA).The system reliability key variables were evaluated using the StatistiXL software.A structured questionnaire was crafted considering thirtythree reliability variables, harvested from literature, and administered to respondents in the micro-grid power system industry.The respondent size was determined at a level of confidence of 95% and an error margin of 5% was deployed to corroborate an adequate population size representation which validated the study data.StatistiXL software was deployed to analyze the (mxn) data matrix, collated from the respondents' scores.The matrix was used as the input variable for the model deployed for the factor analysis.Nine factors with eigenvalues (λ˃1) were mined and labeled for the analysis, but all the trivial variables were discarded.The PCA result holistically pinpointed the key reliability variables that influence the micro-grid reliability, revealing that system availability represented by factor 1 (F1) loaded 24% of the total variables studied, with reliability cluster including Mean Time Between Failures (MTBF) = -0.844,Mean Time to Repair (MTTR) = -0.737,Demand Response (DR) technique = 0.752, Failure Rate = 0.647 among others.The failure rate and the frequency of outages in F1, were an indication that system availability would be influenced, thereby affecting the micro-grid performance.The study also extracted some weak factor loading, F8 and F9 indicting them as reliability variables whose influences do not impact negatively on the micro-grid reliability but should not be discarded in the study of the reliability of micro-grid power systems.Hence an attempt to improve the system's reliability, concentrating on the key variables factors, the weak variables should not be neglected.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.180
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0030.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.033
GPT teacher head0.276
Teacher spread0.243 · 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