针对复杂工况下故障电弧检测存在多模态特征利用率低、噪声鲁棒性差的问题, 该文提出一种基于多模态特征融合的故障电弧检测方法。通过搭建过电流诱发并联击穿电弧的实验电路, 采集不同阻燃等级护套线与过电流参数下的电流时序信号, 构建多模态数据集。基于Markov转换场、递归图等5种时域、频域、时频域转换方法, 将瞬态电流信号映射为时频-空间特征图, 并与一维时序信号结合, 引入交叉注意力机制优化Swin-Transformer网络, 实现跨模态特征协同优化。结果表明, 时频图像增强了对高频暂态特征的敏感性, 注意力机制通过聚焦关键频段和时域片段, 能更有效地捕捉故障电弧特征, 其分类准确率达98.07%, 较传统CNN模型提升了0.37%, 为过电流诱发并联击穿电弧检测提供了更可靠的技术方案。

Objective: Neural network models have shown strong performance in fault arc detection. However, these models often relied on fragmented, single-modality features—such as time-domain, frequency-domain, or time-frequency representations of one-dimensional time series—making it difficult to capture transient high-frequency oscillations at the microsecond level. This resulted in the loss of critical detail, limiting the ability to predict arc occurrence precursors and weakening emergency response. To address this issue, this paper proposed a fault arc detection method based on multi-modality feature fusion. Methods: An experimental circuit simulating parallel breakdown arc induced by overcurrent was built, with 75 effective working conditions designed and over 100, 000 data points collected per scenario. Based on the typical characteristics of pre-fault and fault waveforms, 5, 456 one-dimensional time-series signal samples were constructed. Five conversion methods—Markov transition field (MTF), recurrence plot (RP), Gramian angular field (GAF), short-time Fourier transform (STFT), and continuous wavelet transform (CWT)—were used to convert the transient current signals into time-frequency-space feature maps (TFS-Maps). Each mapping method involved multiple parameters, and their effectiveness in feature extraction varied, necessitating the selection of optimal settings. For MTF, parameters such as the number of bins, interval division strategy, and color mapping scheme were chosen. For GAF, the visualization results of the summation and difference angular fields were compared. For STFT, window lengths of 16, 32, and 64 were tested. For CWT, the wavelet basis, scale, center frequency, and bandwidth were optimized. For RP, the signal's standard deviation was used. The resulting multi-modality dataset—containing original signals and their corresponding TFS-Maps—was split into training, validation, and test sets in a 7∶2∶1 ratio. A gated recurrent unit (GRU) was used to model sequence dependencies in the original signals. A Swin transformer integrated with the convolutional block attention module (Swin Transformer-CBAM) was applied to highlight key regions within the TFS-Maps. The outputs from GRU and Swin Transformer-CBAM were fused via cross-modality concatenation to perform arc detection. Accuracy, precision, recall, F1-score, and comprehensive evaluation visualization graphs were used to assess the algorithm's performance. Results: The experimental results showed that (1) among various TFS-Maps, GADF achieved the highest performance, with 98.07% accuracy, 97.52% F1-score, and 98.01% recall; 2) Swin Transformer-CBAM outperformed the convolutional neural network, with improvements of 0.37% in accuracy, 0.17% in F1-score, and an increase in recall from 97.67% to 98.01%; and (3) the confusion matrix indicated very few misclassifications, with over 98% agreement between predicted and actual labels. Conclusions: Time-frequency imaging enhanced sensitivity to high-frequency transient features. The attention mechanism effectively captured fault arc features by focusing on critical frequency bands and time-domain segments. The proposed detection method met expectations, improved detection efficiency, and provided a more reliable technical solution for identifying parallel breakdown arcs induced by overcurrent.