Integrated Digital Twins System for Oil Temperature Prediction of Power Transformer Based on Internet of Things

Oil temperature is an important index to reflect the state of transformer and predict the fault and remaining useful life of transformer. However, the oil temperature of the transformer is affected by various factors, such as weather conditions, load changes, holidays, seasons, and so on. In order to predict oil temperature more accurately, this study proposes a VCEEMDAN data decomposition algorithm to extract the characteristics of oil temperature data. And we proposed a hybrid long-term series forecasting model of oil temperature based on VCEEMDAN, particle swarm optimization (PSO) and Informer. At the same time, the digital twins system of the manipulator is configured to assist personnel in maintenance when the oil temperature is abnormal. And the digital twins system of an oil-immersed power transformer to monitoring of transformer status. Finally, the above research is integrated into a digital twins system, including transformer condition monitoring, oil temperature prediction, and intelligent maintenance based on Internet of Things (IOT). The proposed prediction method was compared with the six existing models on three datasets. From the experimental results, the best index improvement is 77.97% higher than the second-best model. And the simulation system can provide a good reference for the practical application of the factory. This article presents a hybrid framework that achieves a accurate transformer oil temperature prediction.