An Advanced Power System Modeling Approach for Transformer Oil Temperature Prediction Integrating SOFTS and Enhanced Bayesian Optimization

Accurate prediction of transformer top-oil temperature is crucial for insulation ageing assessment and fault warning. This paper proposes a novel prediction method based on Variational Mode Decomposition (VMD), kernel principal component analysis (Kernel PCA), a Time-aware Shapley Additive Explanations–Multilayer Perceptron (TSHAP-MLP) feature selection method, enhanced Bayesian optimization, and a Self-organized Time Series Forecasting System (SOFTS). First, the top-oil temperature signal is decomposed using VMD to extract components of different frequency bands. Then, Kernel PCA is employed to perform non-linear dimensionality reduction on the resulting intrinsic mode functions (IMFs). Subsequently, a TSHAP-MLP approach—incorporating temporal weighting and a sliding window mechanism—is used to evaluate the dynamic contributions of historical monitoring data and IMF features over time. Features with SHAP values greater than 1 are selected to reduce input dimensionality. Finally, an enhanced hierarchical Bayesian optimization algorithm is used to fine-tune the SOFTS model parameters, thereby improving prediction accuracy. Experimental results demonstrate that the proposed model outperforms transformer, TimesNet, LSTM, and BP in terms of error metrics, confirming its effectiveness for accurate transformer top-oil temperature prediction.