Randomised Field Trial for Risk Reduction in Kenyan Power-Distribution Equipment: A Methodological Evaluation

{ "background": "Power-distribution infrastructure in sub-Saharan Africa faces acute challenges from environmental stressors and ageing assets, leading to frequent failures. While risk-reduction interventions are proposed, robust field evaluations of their efficacy are scarce, particularly within constrained operational budgets. ", "purpose and objectives": "This case study aims to methodologically evaluate the implementation of a randomised field trial (RFT) for assessing technical interventions aimed at reducing failure rates in medium-voltage distribution equipment. It seeks to determine the feasibility and analytical value of RFTs in this real-world, resource-limited context. ", "methodology": "A pragmatic RFT was designed and implemented across multiple service regions. Over 800 distribution transformers and associated switchgear were randomly assigned to either a treatment group, receiving a composite intervention of silicone rubber coating and upgraded surge arrestors, or a control group. Failure events were logged over an extended operational period. The primary analysis used a Cox proportional hazards model: h (t|X) = h0 (t) \ (\1 + \'Z), where Z is a vector of covariates including age and environmental exposure. ", "findings": "The RFT methodology proved feasible but required significant adaptation to local logistical constraints. The intervention group exhibited a hazard ratio of 0. 62 (95% CI: 0. 51 to 0. 75) for equipment failure, indicating a 38% reduction in risk. Uncertainty was quantified using robust standard errors clustered by substation. ", "conclusion": "The structured RFT approach provided a rigorous, evidence-based framework for evaluating engineering interventions in a complex field environment, yielding clear causal inference on effectiveness. ", "recommendations": "Utilities should adopt phased RFT designs for major asset interventions. Future trials should incorporate longer follow-up periods and more granular environmental data collection to refine the model. ", "key words": "randomised field trial, distribution transformers, asset management, failure risk, causal inference, sub-Saharan Africa", "contribution statement": "