Losses and temperature rise directly affect the insulation life and operational reliability of high-power high-frequency transformers. Accurate thermal state perception is crucial to its safe operation. In order to realize fast and accurate on-line monitoring of temperature and loss of high frequency transformer

an on-line calculation model based on physical-data dual drive is proposed. Firstly

an improved thermal network model considering the frequency-dependent effect of heat conduction and thermal anisotropy is constructed

and its high accuracy is verified to be comparable to that of finite element simulation. Then

aimed at the problem that the experimental samples are difficult to measure and the finite element sample generation takes a long time

a physical constraint wasserstein generative adversarial network with gradient penalty(PC-WGAN-GP) is proposed to generate a high-quality data set that conforms to the law of thermodynamics. Finally

a physics-informed convolutional neural network(PI-CNN) model based on crown porcupine optimization(CPO) is constructed. By introducing a hybrid loss function of dynamic weights

the deep fusion of physical mechanism constraints and data-driven features is realized. The results show that the relative error of the hot spot temperature of the key nodes calculated by the improved thermal network model is within 3.31%

and the equivalent physical benchmark is established. On this basis

the relative mean absolute error(RMAE) of the proposed CPO-PI-CNN model on the test set is reduced to 2.66%

and the coefficient of determination R2 is 99.19%

which verifies that the method has good calculation accuracy and generalization ability.