Transformer Health Index Calculation Using Multi Parameter Fusion

This research presents a novel methodology for calculating transformer health indices through multi-parameter fusion techniques, addressing the critical need for accurate condition assessment of power transformers. The proposed framework integrates key transformer health indicators including dissolved gas analysis (DGA), oil quality metrics, historical loading patterns, temperature profiles and thermal aging effects, moisture content analysis, and bushing and insulation condition parameters. Unlike conventional approaches that often rely on isolated parameter analysis, this study employs advanced data fusion algorithms to synthesize these diverse parameters into a comprehensive health index. The research leverages machine learning techniques to appropriately weight each parameter’s contribution to the overall health assessment based on trans- former type, operational environment, and age. Particular at- tention is given to correlating interdependent parameters, such as the relationship between moisture content and insulation degradation, and between loading history and thermal aging. Through case studies on a diverse set of transformers, the proposed methodology demonstrates superior accuracy in failure prediction compared to traditional single-parameter or non- weighted fusion approaches. Research gaps addressed include the integration of real-time monitoring with historical data, developing adaptive weighting mechanisms that evolve with transformer age, and the establishment of standardized health in- dex benchmarks across different transformer classifications. The findings contribute to the advancement of predictive maintenance strategies for critical power infrastructure, potentially extending transformer life expectancy while reducing catastrophic failures.