Thermal state parameter prediction method of 110 kV transformer based on multimodal deep learning

Owing to the limitations of testing methods and the inaccuracy of knowledge, the obtained power transformer information is characterized by fuzziness and randomness, which leads to a lack of accurate description of transformer internal coupling interaction and fault evolution in operation and maintenance departments. Therefore, a thermal state parameter prediction method for a 110 kV transformer based on multi-modal deep learning is designed. The data were resampled and smoothed, and high-frequency noise was removed using a wavelet transform. The running time and spatial temperature rise characteristics of the thermal state parameters of the 110 kV transformer were analyzed, and the variational mode decomposition method was used to decompose the thermal state parameters. By constructing a multimodal deep learning network, abstracted relevant features were extracted from high-dimensional complex input data. By constructing the state equation and measurement equation of the hot spot temperature, a real-time optimal estimation model of the hot spot temperature was established. The extended Kalman filter method was used to build a prediction model for a nonlinear top oil temperature rise system. The experimental results showed that the deviation from the actual value was the least, and the difference between the predicted and measured values of the top oil temperature was relatively small. The prediction time was lower than 0.2s, and the remaining life prediction was correct for 17 units.