Research on a fault diagnosis method for photovoltaic power plants based on a dual-channel 1D-2D-CNN-BiLSTM spatiotemporal feature fusion network

This study proposes a fault diagnosis method for grid-connected photovoltaic (PV) plants based on dual-channel spatiotemporal feature fusion. First, the Gramian Angular Field (GAF) transformation is employed to convert raw monitoring data into two-dimensional structured feature matrices with inherent temporal correlations, forming a two-dimensional feature processing branch. Meanwhile, the original one-dimensional current and voltage time series are retained as a numerical feature branch, establishing a parallel dual-channel processing architecture. A convolutional neural network (CNN) is used to extract local spatial patterns from the two-dimensional matrices, while a bidirectional long short-term memory network (BiLSTM) captures global temporal dependencies from the one-dimensional sequences, and a self-attention mechanism is introduced to dynamically weight key features. Through a bimodal feature complementarity mechanism, this method effectively overcomes the limitation of single-modality feature representation encountered by traditional approaches under complex operating conditions, and verifies its feasibility for engineering applications. Experimental results show that the method achieves an accuracy of 94.6875% in diagnosing seven typical fault types, demonstrating the effectiveness of the proposed approach in detecting and classifying various PV faults.