基于多源融合图与 SE-BiGRU-ResNet 模型的MMC 子模块开路故障诊断
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TH165. 3 TM464 TM407

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Open-circuit fault diagnosis of MMC sub-module based on multi-source fusion graph and SE-BiGRU-ResNet model
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    摘要:

    人工智能算法现广泛应用于模块化多电平换流器(MMC)故障诊断中,但现有算法均需大量目标域样本来训练模型,针 对小样本下难以精确诊断的问题,提出基于多源融合图与 SE-BiGRU-ResNet 模型的 MMC 小样本分立化故障诊断方法。 首先, 根据开路故障特性,选择输出相电流和桥臂电压作为关键故障参量;其次,利用递归图、马尔可夫转移场和格拉姆角场算法将一 维故障参量映射为相应的二维特征图像,为全面加强图像的特征显著性,将各图按通道维度增广叠加为多源融合图;最后,以残 差网络(ResNet)为基础,为提高模型捕捉关键时空特征的能力,引入挤压-激励(SE)模块和双向门控循环单元(BiGRU)模块, 建立 SE-BiGRU-ResNet 模型对多源融合图进行训练和测试。 实验结果表明:相比其他方法,在小样本情况下诊断故障桥臂与定 位子模块中故障 IGBT 的准确率达到 98. 10% 和 99. 13% ,诊断精度高;测试过程拥有秒级响应时间;在极端条件扰动下仍具备 较好的诊断性能与较强的泛化能力。

    Abstract:

    Artificial intelligence algorithms are widely used in fault diagnosis of modular multilevel converter ( MMC). However, the existing algorithms require a large number of target domain samples to train the model. To address the problem that it is difficult to diagnose accurately under small samples, a MMC small sample discrete fault diagnosis method based on a multi-source fusion graph and SE-BiGRU-ResNet model is proposed. Firstly, according to the characteristics of an open-circuit fault, the output phase current and bridge arm voltage is selected as the key fault parameters. Secondly, the 1D fault parameters are mapped into the corresponding 2D feature images by using the recurrence plot, Markov transition field, and the Gramian angular field algorithm. To comprehensively strengthen the feature saliency of the image, the multi-source fusion graph is obtained by adding each graph according to the channel dimension. Finally, based on the residual network (ResNet), to improve the ability of the model to capture key spatiotemporal features, the squeeze-excitation (SE) module and the bidirectional gated recurrent unit (BiGRU) module are introduced. The SE-BiGRU-ResNet model is formulated to train and test the multi-source fusion graph. Compared with other methods, the experimental results show that the accuracy of fault diagnosis of IGBT in the fault bridge arm and positioning sub-module reaches 98. 10% and 99. 13% in the case of small samples, and the diagnostic accuracy is high. The test process has a second-level response time. It still has good diagnostic performance and strong generalization ability under extreme conditions. Keywords:modular multilevel converter; fault diagnosis; sm

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刘述喜.基于多源融合图与 SE-BiGRU-ResNet 模型的MMC 子模块开路故障诊断[J].仪器仪表学报,2024,45(11):322-337

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  • 在线发布日期: 2025-01-26
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