为有效识别多源局部放电现象，解决模型网络复杂性高、微小目标易出现误检和漏检等问题，提出了一种基于改进DQ-BETR的多源局部放电类型识别方法。首先，搭建局部放电多物理信号检测平台，综合运用超声、暂态电压和特高频检测法进行数据测量，收集典型局部放电单源和多源数据，构建局部放电图谱数据集；其次，引入新型视觉主干网络（VManba）替换DQ-DETR原有的ResNet50主干网络，以提取更为丰富的图像特征，并在上述改进基础上引入混合匹配策略，旨在提升DQ-DETR模型训练阶段的效率和解决正样本稀缺问题；最后，利用不同传感器数据进行单源与多源局部放电识别，并采用D-S证据理论对3种传感器识别结果进行融合。实验结果表明：在3种不同传感器数据上均取得了良好的识别效果，融合结果相较于单一传感器识别结果，性能指标P、R、F₁及mAP（IOU=0.5）平均提升了2.03%、2.01%、1.95%和1.76%，同时较SSD、Yolov5、Fast RCNN、DETR、Anchor DETR和Conditional DETR等常用局部放电识别方法，所提方法的P、R、F₁及mAP（IOU＝0.5）性能指标平均提升了1.52%、8.24%、5.07%和2.99%。由此可知，所提方法不仅能有效识别单源与多源局部放电的类别，还显著提升了局部放电类别辨识的收敛速度和精确度等性能，且具备良好的运算效率和通用性。

In order to effectively identify the multi-source partial discharge phenomenon and solve the problems of high complexity of the model network, false detection and missed detection of small targets, a multi-source partial discharge type identification method based on improved DQ-BETR was proposed. Firstly, a multi physical signal detection platform for partial discharge was built, and the data were measured using ultrasonic, transient voltage and UHF detection methods. The typical single source and multi-source data of partial discharge were collected, and the partial discharge spectrum data set was constructed. Secondly, a new visual backbone network(VManba) was introduced to replace the original ResNet50 backbone network of DQ-DETR to extract more abundant image features. Based on the above improvements, a hybrid matching strategy was introduced to improve the efficiency of the training phase of DQ-DETR model and solve the problem of scarcity of positive samples. Finally, single source and multi-source partial discharge were identified by using different sensor data, and the identification results of the three sensors were fused by using D-S evidence theory. The experimental results show that good recognition results are achieved on the three different sensor data. Compared with the single sensor recognition results, the performance indexes of P, R, F₁ and mAP (IOU = 0.5) are improved by 2.03%, 2.01%, 1.95% and 1.76% on average. At the same time, compared with SSD, Yolov5, fast RCNN, DETR, anchor DETR and conditional DETR, the performance indexes of P, R, F₁ and mAP (IOU = 0.5) of the proposed method are improved by 1.52%, 8.24%, 5.07% and 2.99% on average. Therefore, the proposed method can not only effectively identify the categories of single source and multi-source partial discharge, but also significantly improve the convergence speed and accuracy of partial discharge category identification, and has good computational efficiency and versatility.