Quantitative Simulation-Based Model for Short-Circuit Analysis, Arc-Flash Risk Evaluation, and Protection Coordination in Industrial Electrical Systems
Bibliographic record
Abstract
This study addresses the persistent safety and reliability problem in industrial electrical systems where short-circuit faults, arc-flash hazards, and poorly coordinated protective devices can jointly cause equipment damage, worker injury, unnecessary outages, and reduced operational continuity. The purpose of the research was to develop and test an integrated quantitative simulation-based model showing how short-circuit analysis, arc-flash risk evaluation, and protection coordination influence industrial electrical system safety and performance. Using a quantitative, cross-sectional, case-based design, the study combined survey data from 210 valid respondents drawn from industrial enterprise case environments, including electrical engineers, maintenance engineers, safety officers, technicians, and supervisors, with simulation outputs from key electrical locations such as the main LV switchboard, MCCs, and feeder buses. The main independent variables were short-circuiting analysis, arc-flash risk evaluation, and protection coordination, while the dependent variable was industrial electrical system safety and operational performance. Data were analyzed using descriptive statistics, Pearson correlation, and multiple regression. The findings showed high mean scores for all major constructs, including short-circuit analysis (M = 4.18, SD = 0.61), arc-flash risk evaluation (M = 4.24, SD = 0.57), protection coordination (M = 4.31, SD = 0.54), and industrial electrical safety and performance (M = 4.27, SD = 0.59). Simulation results identified the Main LV Switchboard Bus as the highest fault-current location at 31.6 kA, while arc-flash incident energy at MCC-1 decreased from 9.8 cal/cm² to 5.9 cal/cm² after coordination refinement, a reduction of about 39.8%, and clearing time improved from 0.42 s to 0.24 s. Correlation analysis revealed strong positive relationships with system safety and performance for short-circuit analysis (r = 0.71), arc-flash risk evaluation (r = 0.76), and protection coordination (r = 0.83), all at p < .001. Regression results further showed that the model explained 72.4% of the variance in safety and performance (R² = 0.724, F(3,206) = 180.24, p < .001), with protection coordination emerging as the strongest predictor (β = 0.401), followed by arc-flash risk evaluation (β = 0.287) and short-circuit analysis (β = 0.249). The study implies that industrial organizations can significantly improve electrical safety and system dependability by integrating fault studies, arc-flash assessment, and coordination review into a single protection management framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".