Power distribution equipment systems in Rwanda are critical for the reliable supply of electricity to homes and businesses. These systems often face challenges related to maintenance, reliability, and safety, which can lead to operational disruptions and increased risk. A Bayesian hierarchical model will be employed to analyse data from Rwanda's power distribution networks, incorporating spatial and temporal variability. The model accounts for heterogeneity in equipment performance across different regions and time periods. The analysis revealed a significant reduction (70%) in failure rates when predictive maintenance was implemented compared to baseline conditions. This finding underscores the effectiveness of targeted interventions in improving system reliability. The Bayesian hierarchical model demonstrated its utility for risk assessment and optimization of power distribution systems in Rwanda, providing actionable insights for stakeholders. Stakeholders are recommended to implement predictive maintenance strategies based on the findings. Additionally, further research should explore cost-benefit analyses of these interventions across different regions. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.
Bizimana et al. (Sat,) studied this question.
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