Abstract
Digital twin (DT) in the power industry faces challenges due to complex structures and limited distributed sensors for power equipment. In this work, a novel approach combining virtual-real sensing (VRS) was used to build DT for transformers, taking the degree of polymerization (DP) distribution as an example. Temperature and moisture distributions were calculated, and a dynamic deduction model for DP distribution was established. The visualization of DP distribution was achieved through surface-rebuild. Results showed a 3.5% error in temperature distribution compared to monitoring data. Moisture distribution was concentrated in the cardboard and bottom solid insulation, reaching 3.2% in the 48th year. DP distribution pattern aligned with Cigre’s report, with the lowest value at the hot spot location of the LV windings, deduced to be 252.15 in the 48th year. This study provides a data base for realizing transformers DT and is a key step towards building a vital DT transformer.
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