Chen Xiaojuan, Zhang Zhaohua
School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel). 2024 Feb 5;24(3):1028. doi: 10.3390/s24031028.
Compared with traditional two-level inverters, multilevel inverters have many solid-state switches and complex composition methods. Therefore, diagnosing and treating inverter faults is a prerequisite for the reliable and efficient operation of the inverter. Based on the idea of intelligent complementary fusion, this paper combines the genetic algorithm-binary granulation matrix knowledge-reduction method with the extreme learning machine network to propose a fault-diagnosis method for multi-tube open-circuit faults in T-type three-level inverters. First, the fault characteristics of power devices at different locations of T-type three-level inverters are analyzed, and the inverter output power and its harmonic components are extracted as the basis for power device fault diagnosis. Second, the genetic algorithm-binary granularity matrix knowledge-reduction method is used for optimization to obtain the minimum attribute set required to distinguish the state transitions in various fault cases. Finally, the kernel attribute set is utilized to construct extreme learning machine subclassifiers with corresponding granularity. The experimental results show that the classification accuracy after attribute reduction is higher than that of all subclassifiers under different attribute sets, reflecting the advantages of attribute reduction and the complementarity of different intelligent diagnosis methods, which have stronger fault-diagnosis accuracy and generalization ability compared with the existing methods and provides a new way for hybrid intelligent diagnosis.
与传统两电平逆变器相比，多电平逆变器具有多个固态开关且组成方式复杂。因此，诊断和处理逆变器故障是逆变器可靠高效运行的前提条件。基于智能互补融合的思想，本文将遗传算法 - 二进制粒化矩阵知识约简方法与极限学习机网络相结合，提出了一种T型三电平逆变器多管开路故障的诊断方法。首先，分析了T型三电平逆变器不同位置功率器件的故障特征，提取逆变器输出功率及其谐波分量作为功率器件故障诊断的依据。其次，采用遗传算法 - 二进制粒度矩阵知识约简方法进行优化，得到区分各种故障情况下状态转移所需的最小属性集。最后，利用核属性集构建具有相应粒度的极限学习机子分类器。实验结果表明，属性约简后的分类准确率高于不同属性集下所有子分类器的准确率，体现了属性约简的优势以及不同智能诊断方法的互补性，与现有方法相比具有更强的故障诊断准确率和泛化能力，为混合智能诊断提供了新途径。
