Research on insulation defect diagnosis technology for oil filled transformers based on digital twins

With the continuous upgrading and expansion of substations, the number and service life of oil filled transformers have increased, which increases the probability of insulation defects in oil filled transformers. If insulation defects are not discovered and dealt with in a timely manner, it will bring huge economic losses to the electrical system. Traditional insulation defect diagnosis technology for oil filled transformers is not intuitive and accurate enough, and cannot achieve online monitoring. This article proposes a defect diagnosis technology for oil filled transformers based on digital twins. By installing various parameter transformers on real transformers and visualizing the modeling of real oil filled transformers, a knowledge graph corresponding to insulation defects of transformers is established when each parameter is abnormal. Then, through continuous learning through AI, online monitoring and diagnosis of insulation defects in oil filled transformers are achieved.