Methodological Evaluation and Panel-Data Estimation for Risk Reduction in Nigerian Power-Distribution Systems

Power-distribution systems in Nigeria face persistent reliability challenges, with ageing infrastructure and operational inefficiencies leading to frequent outages and safety risks. A systematic, data-driven approach to quantify and mitigate these risks is required for targeted infrastructure investment. This study aims to develop and validate a methodological framework for evaluating the performance of distribution equipment and to estimate the causal effect of targeted interventions on system-wide risk reduction. A panel-data econometric model was applied to a novel longitudinal dataset of technical inspections and failure records from 18 distribution companies. The core specification is a fixed-effects model: $Risk_{it} = \alpha_i + \beta_1 Intervention_{it} + \beta_2 Age_{it} + \beta_3 Load_{it} + \epsilon_{it}$, where $\alpha_i$ denotes entity-specific effects. Inference is based on cluster-robust standard errors. The implementation of condition-based maintenance protocols was associated with a 22.5% reduction in the predicted risk index for treated substations (95% CI: 18.1% to 26.9%). Transformer age and peak load deviation were also statistically significant positive predictors of failure risk. The proposed panel-data methodology provides a robust tool for performance evaluation, demonstrating that data-informed maintenance strategies can significantly enhance the reliability of distribution networks. Distribution network operators should adopt panel-data analytics for asset management prioritisation. Regulatory bodies should incentivise the collection and standardised reporting of high-frequency equipment performance data. power distribution, asset management, reliability engineering, fixed-effects model, infrastructure risk, maintenance optimisation This paper provides a novel application of panel-data econometrics to power-system asset management, creating a transferable framework for causal inference on intervention effectiveness using operational data.