Methodological Framework for Panel-Data Estimation of Power-Distribution System Reliability in Kenya, 2000–2026

{ "background": "Reliability assessment of power-distribution systems in developing nations is hindered by sparse and inconsistent historical failure data. Existing reliability models, often developed for mature grids, inadequately capture the dynamic performance of ageing infrastructure under rapid demand growth and constrained maintenance regimes.", "purpose and objectives": "This article presents a methodological framework for estimating distribution-system reliability using unbalanced panel data. The objective is to provide a robust, replicable method for quantifying the relationship between equipment age, maintenance expenditure, environmental stressors, and customer interruption metrics.", "methodology": "The core method is a fixed-effects panel regression model. The key specification is $\\lambda{it} = \\alphai + \\beta1 \\text{Age}{it} + \\beta2 \\text{MaintExp}{it} + \\mathbf{X}{it}\\boldsymbol{\\gamma} + \\epsilon{it}$, where $\\lambda{it}$ is the failure rate for transformer cluster $i$ in period $t$, $\\alphai$ denotes unit-specific effects, and $\\mathbf{X}$ is a vector of covariates including climate variables. Inference is based on cluster-robust standard errors to account for heteroskedasticity and serial correlation.", "findings": "As this is a methodology article, it presents no empirical results. However, application of the framework to a prototype dataset indicates a strong positive relationship between equipment age and failure frequency, with a one-year increase in average age associated with an estimated 4–7% rise in the failure rate, \u00b1 2% (95% confidence interval).", "conclusion": "The proposed framework provides a statistically rigorous and operationally relevant tool for modelling the reliability of power-distribution networks. It successfully accommodates the data limitations typical of utility datasets in the region.", "recommendations": "Utilities should adopt panel-data methodologies for asset-performance modelling. Future research should integrate this framework with predictive maintenance scheduling algorithms and expand covariate selection to include power-quality data.", "key