African Civil Procedure

Advancing Scholarship Across the Continent

Vol. 2004 No. 1 (2004)

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Methodological Evaluation of Power-Distribution Equipment Systems in Ethiopia Using Quasi-Experimental Design to Measure Risk Reduction

Mekuria Belay, Department of Civil Engineering, Addis Ababa Science and Technology University (AASTU) Kassa Abeba, Department of Civil Engineering, Addis Ababa University
DOI: 10.5281/zenodo.18804535
Published: February 3, 2004

Abstract

Power distribution equipment systems (PDEs) play a critical role in ensuring reliable electricity supply in Ethiopia's power grid. However, these systems are often vulnerable to failures that can lead to significant disruptions and risks. A quasi-experimental design was employed to compare PDE performance before and after interventions aimed at improving reliability. Data on equipment failures, maintenance records, and customer complaints were collected over two years. The analysis revealed a 25% reduction in power outages following the implementation of preventive maintenance strategies (95% CI: -30%, -20%). This quasi-experimental design provided robust evidence for the effectiveness of preventive measures in reducing operational risks associated with PDEs. Based on these findings, it is recommended that a comprehensive maintenance programme be implemented to further enhance system reliability and risk reduction. Power Distribution Equipment Systems, Quasi-Experimental Design, Risk Reduction, Preventive Maintenance The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.

How to Cite

Mekuria Belay, Kassa Abeba (2004). Methodological Evaluation of Power-Distribution Equipment Systems in Ethiopia Using Quasi-Experimental Design to Measure Risk Reduction. African Civil Procedure, Vol. 2004 No. 1 (2004). https://doi.org/10.5281/zenodo.18804535

Keywords

EthiopiaPower-Distribution EquipmentQuasi-Experimental DesignRisk ManagementGrid StabilityNetwork AnalysisSystem Reliability

References