Abstract:

Aiming at the issues in fault diagnosis of energy Internet such as long model training time， insufficient extraction of fault features， and low diagnostic accuracy with limited training sample size， a Hierarchical Clustering and Multi-Head attention based Convolutional neural network （HCMHC） model was proposed. In the model， the novel Hierarchical Clustering （HC） model was adopted to reduce data redundancy effectively， while Convolutional Neural Network （CNN） and multi-head attention were combined for more accurate and comprehensive fault feature extraction. Furthermore， a contrastive learning model was employed to enhance the complementarity among features with limited training sample size， thereby improving model generalization ability and diagnostic accuracy on new data. Experimental verification results on the New England test system with 39 buses and 10 generators demonstrate that the HCMHC model achieves accuracies of 99.8% and 99.5% on two different datasets respectively， which have improvements of 4.3 and 4.5 percentage points approximately and respectively compared to the Multiple-Input CNN （MI-CNN） model. Additionally， even with a training set/validation set ratio of 20/80， this model still has accuracies of 98.3% and 95.8% on two datasets respectively. The above proves the significant effectiveness and superiority of the proposed model in the field of fault diagnosis.

Key words: fault diagnosis, data fusion, contrastive learning, multi-head attention, complementary fault feature

摘要：

针对能源互联网故障诊断时存在的模型训练时间长、故障特征提取不充分以及在训练样本数量有限的情况下诊断准确率低等问题，提出一种基于层次聚类和多头注意力的多任务卷积神经网络（HCMHC）模型。该模型通过采用新颖的层次聚类（HC）模型有效减少数据的冗余；同时结合了卷积神经网络（CNN）和多头注意力，更准确地提取出更全面的故障特征；此外，采用对比学习模型来在训练样本数量有限时增强特征间的互补性，进而提升模型对新数据的泛化处理能力和诊断准确度。在具有39条母线和10台发电机的新英格兰测试系统上进行的实验结果表明，HCMHC模型在两个不同的数据集上分别实现了99.8%和99.5%的准确率，相较于多输入CNN （MI-CNN）模型分别提高了约4.3和4.5个百分点。此外，即使在训练集与验证集的比例为20/80时，该模型仍然在两个数据集上分别达到了98.3%和95.8%的准确率。可见，所提模型在故障诊断领域中具有显著的有效性和优越性。

关键词: 故障诊断, 数据融合, 对比学习, 多头注意力, 互补故障特征