【摘要】 为提升220 kV变电站GIS设备套管故障诊断中特征提取的有效性，并缓解现场标注样本稀缺带来的模型训练困难，提出基于PKO-VMD特征增强与半监督时空融合网络的故障诊断方法。首先，利用翠鸟优化算法(pied kingfisher optimizer, PKO)对变分模态分解关键参数进行自适应寻优，实现非平稳监测信号的高精度分解与敏感模态增强；随后，构建卷积神经网络-双向门控循环单元-注意力机制(convolutional neural network-bidirectional gated recurrent unit-attention, CNN-BiGRU-Attention)深度融合网络，提取多模态信号的局部特征、双向时序依赖及关键判别信息；最后，引入由监督项、一致性约束项与熵最小化项组成的半监督学习框架，协同利用少量有标签样本与大量无标签样本完成模型训练。在220 kV变电站GIS设备套管监测数据集上的实验结果表明，所提方法的故障诊断准确率达到96.7%,较对比方法具有更优的综合性能；在有标签样本仅占20%的条件下，仍可保持94.2%的诊断精度。结果表明，所提方法能够有效提升GIS设备套管故障诊断的准确性、稳定性与泛化能力，为电力设备智能运维提供了一种可行的技术途径。更多还原

【Abstract】 To improve the effectiveness of feature extraction in bushing fault diagnosis of 220 kV substation GIS equipment and alleviate the difficulty of model training caused by the scarcity of labeled samples, a fault diagnosis methods based on PKO-VMD feature enhancement and a semi-supervised spatiotemporal fusion network is proposed. First, the Pied Kingfisher Optimizer(PKO) algorithm is used to adaptively optimize key parameters of variational mode decomposition, achieving high-precision decomposition and sensitive mode enhancement of non-stationary monitoring signals. Then, a CNN-BiGRU-Attention deep fusion network is constructed to extract local features, bidirectional temporal dependencies, and key discriminant information of multimodal signals. Finally, a semi-supervised learning framework consisting of a supervised term, a consistency constraint term, and an entropy minimization term is introduced to collaboratively utilize a small number of labeled samples and a large number of unlabeled samples to complete model training. Experimental results on the 220 kV substation GIS equipment bushing monitoring dataset show that the proposed method achieves a fault diagnosis accuracy of 96.7%, demonstrating superior overall performance compared to comparative methods; even with only 20% labeled samples, it still maintains a diagnostic accuracy of 94.2%. The results show that the proposed method can effectively improve the accuracy, stability and generalization ability of bushing fault diagnosis for GIS equipment, providing a feasible technical approach for intelligent operation and maintenance of power equipment.更多还原