Transformer Oil Temperature Prediction Method Based on Causal Discovery and GNN-LSTM Model

Transformer top oil temperature prediction is a research focal point in online monitoring of transformer operational status. Existing methods lack interpretability in feature selection and do not consider the temporal correlation of features. To address these issues, we provide a top oil temperature of electric transformers prediction method by using causal discovery and the Graph Neural Network (GNN)-Long Short-Term Memory (LSTM) model in this paper. To conduct feature selection, we use causal discovery to reduce feature dimensionality. Then, construct causal graph data based on the causal relationship matrix. Finally, spatiotemporal features are extracted by the GNN-LSTM model for oil temperature prediction. Experimental results demonstrate that this method can scientifically carry out feature selection, ensuring prediction accuracy and result robustness.