Accurate and rapid fault diagnosis is paramount to stabilizing proton exchange membrane fuel cells (PEMFC). To achieve this, this study proposes a novel fault diagnosis method that integrates a convolutional neural network (CNN), a bi-directional long short-term memory network (BiLSTM), and a waveform fault attention (WFA) mechanism. In the proposed framework, data are classified into five distinct categories utilizing a hierarchical clustering algorithm. Additionally, data augmentation techniques are implemented to bolster model performance. The introduction of amplitude attention and temporal difference attention, in conjunction with the construction of WFA, enables the accurate extraction of temporal-spatial features, significantly improving the distinguishability of fault diagnosis. Furthermore, feature contribution is evaluated using permutation feature importance (PFI) to identify key features, enhancing the interpretability of the model. Experimental findings verify that the proposed method enables high-precision fault identification, with precision values spanning 97–100% and an average stability of 98.3%, demonstrating robust performance even when the volume of original sample data is limited. This performance markedly surpasses that of extant methodologies. The comprehensive approach augments the accuracy, reliability, and interpretability of PEMFC fault diagnosis, and introduces a novel research paradigm for feature extraction, thereby possessing significant theoretical and practical application value.

With the ongoing energy transition, large-scale integration of inverter-based renewable generation at DC sending ends has significantly weakened grid strength and increased vulnerability to disturbances from the DC receiving end. These disturbances may trigger severe transient voltage variations and synchronization instability of renewable energy converters, especially under weak-grid conditions where conventional fault ride-through schemes become ineffective. To address this challenge, this paper establishes a mathematical model of a high-renewable-penetrated sending-end system with DC transmission and analytically derives the converter stability boundaries under different grid strengths and fault severities. Based on the identified stability region, a virtual power-angle increment feedback control strategy is proposed to suppress transient instability and mitigate voltage impacts. The effectiveness and practical feasibility of the proposed method are validated through Simulink simulations and RT-LAB hardware-in-the-loop experiments. The results demonstrate that the proposed approach enhances synchronization robustness and provides an effective solution for secure power delivery in future renewable-dominated systems.