Power transformer online monitoring and state assessment method based on 2DCNN-BiLSTM multi-source feature fusion

As key power system equipment, real-time monitoring and accurate assessment of power transformers' operating status are critical for grid safety. To address deficiencies in existing methods regarding multi-source heterogeneous data fusion, unstructured information processing, and small-sample fault recognition, this paper proposes a multi-source deep feature extraction and fusion method combining two-dimensional convolutional neural networks (2D-CNN) and bidirectional long short-term memory networks (BiLSTM) for online transformer status monitoring. A multi-channel sensor platform collects key variables (e.g., partial discharge acoustic signals, voltage, current, oil-dissolved gas concentration) to acquire and standardize spatial and temporal features. A dual-channel model is designed: 2D-CNN extracts spatial features from images, BiLSTM captures temporal dependencies, with an attention mechanism weighting and fusing these features, followed by a fully connected layer. A Softmax classifier with ensemble learning performs state discrimination to enhance stability and generalization. Experiments using real data from east China power grid Dongwu Station transformers for classifying five typical states show the method outperforms traditional and single-modal deep models in accuracy, achieving effective transformer status monitoring.