A new method for transformer hot-spot temperature prediction based on dynamic mode decomposition

The Accurate prediction of a hot spot temperature (HST) is critical for ensuring the reliable operation of transformers. The existing HST prediction methods are based on the black-box model. They cannot take into account both the real-time prediction and the acquisition of the winding temperature field, and the prediction results are unexplained. Therefore, a new HST prediction method based on dynamic mode decomposition (DMD) is proposed in this study. First, the electromagnetic-thermal-flow coupling model of a transformer winding temperature field was established for a transient calculation. A simulation snapshot set of the winding temperature field was obtained. DMD and DMD dominant modes selection were then performed on the snapshot set. Finally, the HST and winding temperature field distributions at the subsequent times were predicted by the DMD dominant modes and their revolution laws. By comparing the prediction results of the HST and winding temperature field distribution with the simulation and experimental measurement results, the speed and accuracy of the proposed prediction method were verified. The DMD-based prediction method can predict the HST and winding temperature field distribution in a few seconds and has a clear physical meaning.

Automated summary

This study proposed a new HST prediction method based on dynamic mode decomposition (DMD) which can accurately predict transformer's hot spot temperature (HST) and winding temperature field distribution in a few seconds with clear physical meaning.