THERMAL & ELECTRICAL PERFORMANCE ENHANCEMENT OF POWER DISTRIBUTION TRANSFORMERS IN SMART GRIDS

Abstract

This study addresses the persistent problem of thermal stress, electrical losses, and premature failures in power distribution transformers operating in smart grid environments, where dynamic loading and high reliability expectations intensify asset risk. The purpose is to quantify how smart grid oriented operational, monitoring, and maintenance practices enhance transformer thermal and electrical performance and reliability. Using a quantitative, cross-sectional, case-based survey design, data were collected from 210 engineers, operators, maintenance supervisors, and asset managers across eight smart grid enabled utility and industrial enterprises, using a five-point Likert scale instrument with construct reliabilities between 0.86 and 0.91. Key independent variables were smart monitoring and automation, thermal management and cooling practices, load management and demand response, and predictive maintenance and diagnostics, while dependent variables captured perceived thermal performance, electrical performance and efficiency, and reliability and lifespan outcomes. Data were analyzed using descriptive statistics, Pearson correlations, and multiple regression. Headline findings show all practice constructs are positively and significantly related to performance, with correlations between 0.53 and 0.71 and regression models explaining 53–60 percent of variance in outcomes; predictive maintenance (β = 0.38, p < .001) and smart monitoring (β = 0.30–0.31, p < .001) emerge as the strongest predictors of reliability and thermal performance, while thermal management most strongly predicts electrical efficiency (β = 0.34, p < .001). Organizations with more than five years of smart grid deployment report significantly higher practice levels and reliability scores (mean reliability 4.05 versus 3.71, p = .001). The findings imply that integrated investment in smart monitoring, disciplined thermal and load management, and predictive maintenance is critical for extending transformer life and improving smart grid resilience.