In this study, the open-circuit faults diagnosis and location issue of the neutral-point-clamped (NPC) inverters are analysed. A novel fault diagnosis approach based on knowledge driven and data driven was presented for the open-circuit faults in insulated-gate bipolar transistors (IGBTs) of NPC inverter, and Concordia transform (knowledge driven) and random forests (RFs) technique (data driven) are employed to improve the robustness performance of the fault diagnosis classifier. First, the fault feature data of AC in either normal state or open-circuit faults states of NPC inverter are analysed and extracted. Second, the Concordia transform is used to process the fault samples, and it has been verified that the slopes of current trajectories are not affected by different loads in this study, which can help the proposed method to reduce overdependence on fault data. Moreover, then the transformed fault samples are adopted to train the RFs fault diagnosis classifier, and the fault diagnosis results show that the classification accuracy and robustness performance of the fault diagnosis classifier are improved. Finally, the diagnosis results of online fault diagnosis experiments show that the proposed classifier can locate the open-circuit fault of IGBTs in NPC inverter under the conditions of different loads.

中文翻译：

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基于知识驱动和数据驱动的NPC逆变器开路故障诊断

在这项研究中，分析了中性点钳位（NPC）逆变器的开路故障诊断和位置问题。针对NPC逆变器的绝缘栅双极晶体管（IGBT）开路故障，Concordia变换（知识驱动）和随机森林（RF）技术（数据驱动），提出了一种基于知识驱动和数据驱动的新型故障诊断方法。 ）用于提高故障诊断分类器的鲁棒性。首先，分析并提取NPC逆变器在正常状态或开路故障状态下的交流故障特征数据。其次，Concordia变换用于处理故障样本，并且已验证了本研究中电流轨迹的斜率不受不同负载的影响，这可以帮助所提出的方法减少对故障数据的过度依赖。此外，采用变换后的故障样本对射频故障诊断分类器进行训练，故障诊断结果表明，改进了故障诊断分类器的分类精度和鲁棒性。最后，在线故障诊断实验的诊断结果表明，该分类器可以在不同负载条件下定位NPC逆变器中IGBT的开路故障。