Abstract
{ "background": "Power-distribution reliability in sub-Saharan contexts is often assessed using theoretical models, with limited validation through structured field experiments. The original quasi-experimental study proposed a novel framework for on-site diagnostics, yet its methodological robustness and practical applicability required independent verification.", "purpose and objectives": "This study aimed to replicate a quasi-experimental design for evaluating the reliability of medium-voltage distribution equipment. The objectives were to assess the reproducibility of the diagnostic protocol, verify the statistical model for failure prediction, and evaluate the method's operational feasibility in a real-world setting.", "methodology": "We executed a matched-pairs quasi-experiment, replicating the intervention and control group structure across multiple rural and peri-urban feeders. Diagnostic data from circuit breakers, transformers, and isolators were collected. The core reliability metric was modelled using a Weibull survival function: $R(t) = \\exp^{-(t/\\eta)^\\beta}$, where $\\eta$ is the scale parameter and $\\beta$ the shape parameter. Inference was based on 95% confidence intervals derived from bootstrapped standard errors.", "findings": "The replication confirmed the original study's central finding that diagnostic interventions significantly improved predicted mean time to failure. Specifically, the hazard ratio for the intervention group was 0.65 (95% CI: 0.52 to 0.81), indicating a 35% reduction in the instantaneous risk of failure. However, the estimated shape parameter $\\beta$ differed, suggesting a more pronounced wear-out failure mode in the replicated sample.", "conclusion": "The quasi-experimental design is methodologically sound and replicable for field-based reliability diagnostics. While the core benefit of the intervention was confirmed, parameter variations highlight context-dependent failure characteristics that must be accounted for in predictive models.", "recommendations": "Utilities should adopt this replicated diagnostic framework for targeted maintenance planning. Further research should focus on calibrating the survival model parameters with longer-term operational data to enhance localised accuracy.", "key