The hot-spot temperature of oil-immersed transformers is one of the important factors affecting the insulation life of power transformers. The adverse impact and installation inconvenience may be aroused when measuring the hot-spot temperature by the distributed optical fiber sensor temperature measurement system

therefore

we proposed an oil-immersed transformer hot spot temperature prediction model based on fuzzy neural network algorithm. Taking 66 kV three-phase three-column oil-immersed self-cooled transformer as an example

we adopted the finite volume method to iteratively solve the transformer fluid-temperature field and determine the hot spot temperature range. The fuzzy subsets of 12 characteristic points such as ambient temperature

transformer top oil temperature

transformer casing and fuel tank top were screened

and the temperature of the transformer winding hot spot was predicted by the T-S fuzzy neural network algorithm. The results show that the temperature error of the winding hot spot measured with the fiber optic sensor is within 4% and it has higher precision than traditional method. This method realizes the non-implantable characteristic measurement and provides a new method for the calculation of hot spot temperature in power transformer on-line monitoring.