Methodological Evaluation and Yield Improvement Diagnostics for Power-Distribution Equipment in Rwanda: A Quasi-Experimental Data Set, 2000–2026

{ "background": "The reliability and efficiency of power-distribution equipment are critical for sustainable development, yet longitudinal performance data from operational networks in sub-Saharan Africa remain scarce. This gap hinders evidence-based asset management and targeted infrastructure investment.", "purpose and objectives": "This data descriptor presents a structured, quasi-experimental dataset designed to methodologically evaluate the performance of distribution equipment and diagnose yield improvement potential. The primary objective is to provide a robust empirical foundation for engineering analyses of failure rates, maintenance efficacy, and upgrade interventions.", "methodology": "A quasi-experimental design was implemented, comparing treatment groups (equipment subjected to specific maintenance or upgrade protocols) with matched control groups across the national network. Data collection integrates supervisory control and acquisition (SCADA) logs, maintenance records, and periodic field measurements. The core analytical model is a difference-in-differences specification: $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\beta3 (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, with inference based on cluster-robust standard errors.", "findings": "The dataset enables diagnostic analysis of equipment yield; preliminary exploration indicates a marked reduction in unscheduled outages for treated assets, with a central estimate suggesting an improvement of approximately 15 percentage points in operational availability. The uncertainty around this estimate, represented by a 95% confidence interval, underscores the value of the full dataset for precise inference.", "conclusion": "This curated dataset provides a novel, methodologically rigorous resource for evaluating power-distribution equipment performance under real-world operational conditions. It establishes a framework for causal analysis of engineering interventions.", "recommendations": "Researchers should utilise the quasi-experimental structure to isolate treatment effects. Practitioners can apply the diagnostic framework to prioritise infrastructure upgrades. Future data collection should incorporate real-time condition monitoring sensors.", "key words": "power distribution, infrastructure diagnostics,