Research on open-circuit fault diagnosis of three-level inverter
-
摘要: 针对传统三电平逆变器开路故障诊断方法存在计算复杂、准确率低等问题，提出了一种基于小波分析与粒子群优化支持向量机的三电平逆变器开路故障诊断方法（WT-PSO-SVM）。在分析三电平逆变器三相电流信号特征的基础上，利用三层小波对电流信号进行分解，提取各频带能量作为故障特征；小波变换提取到故障能量特征后，部分故障下所提取的能量十分接近，无法有效区分，进而引入正半周比例系数作为辅助特征；将归一化的能量和正半周比例系数作为特征向量输入支持向量机进行分类训练，同时利用粒子群算法优化支持向量机的参数以达到最好的分类效果，最终实现故障诊断。实验结果表明：WT-PSO-SVM方法可以有效识别三电平逆变器的开路故障，较其他故障诊断方法有更高的诊断精度和速度，在变负载和有噪声影响情况下仍有较高的故障识别准确率，准确率达到97.3%。Abstract: In view of problems of complicated calculation and low accuracy existed in traditional open-circuit fault diagnosis methods of three-level inverter, an open-circuit fault diagnosis method of three-level inverter based on wavelet analysis and particle swarm optimization support vector machine (WT-PSO-SVM) was proposed. On the basis of analyzing the characteristics of the three-phase current signal of the three-level inverter, the current signal is decomposed by using the three-layer wavelet, and the energy of each frequency band is extracted as the fault feature. After the energy was extracted by wavelet transform, the extracted energy under partial faults is very close and cannot be distinguished effectively, and then the positive half-cycle proportional coefficient is introduced as auxiliary feature. The normalized energy and the positive half-cycle proportional coefficient are used as feature vectors to input support vector machines for classification training, and the parameters of support vector machine are optimized by particle swarm optimization algorithm to achieve the best classification effect, so as to realize fault diagnosis. The experimental results show that the WT-PSO-SVM method can effectively identify open-circuit faults of the three-level inverter, which has higher diagnostic accuracy and speed than other fault diagnosis methods, and still has a higher fault identification accuracy of 97.3% in the case of variable load and noise.
-
-
期刊类型引用(12)
1. 史小军，于铄航，王伟，公铮. 基于直挂储能控制的煤矿交直流混合配电网电压波动抑制研究. 工矿自动化. 2025(01): 78-84+94 . 
本站查看
2. 贾俊青，武文丽，蔡文超，杨洋，梁帅. 基于PSO的电气设备绝缘故障诊断系统设计. 电子设计工程. 2024(01): 77-81 . 百度学术
3. 李冉，邢砾云，庞娜，沈建强，王策. 基于SMA-VMD和优化神经网络的逆变器开关故障诊断. 电子测量技术. 2024(10): 1-9 . 百度学术
4. 王晓鹏，姚帅亮，姚芳，董超群. 逆变器功率管开路故障诊断方法综述. 电源学报. 2023(03): 156-169 . 百度学术
5. 曹茂琨，帕孜来·马合木提. 基于CEEMD-SE的三电平逆变器复杂故障诊断. 现代电子技术. 2022(02): 73-78 . 百度学术
6. 陈丽晶，张尚田，单添敏，姚晓涵，曹亮，王景霖. 基于多分类SVM的航空逆变器故障诊断. 测控技术. 2022(06): 46-50 . 百度学术
7. 程硕，帕孜来·马合木提. 基于键合图和贝叶斯网络的NPC逆变器故障诊断. 机床与液压. 2022(15): 207-212 . 百度学术
8. 梁宏. 矿用NPC三电平逆变器开关管开路故障诊断方法. 工矿自动化. 2022(10): 142-150 . 本站查看
9. 符士侃，夏元轶，杜钰，石廷川. 基于小波变换的电子档案查阅系统电路故障自动诊断研究. 自动化与仪器仪表. 2022(11): 275-280 . 百度学术
10. 邹序焱，蒋利娜. 模糊聚类算法对受众性格的分类分析. 宜宾学院学报. 2022(12): 93-98 . 百度学术
11. 赵俊栋，成庶，刘嘉文，伍珣，于天剑，向超群. 快捷货运机车变流系统智能故障诊断技术综述. 冶金管理. 2021(01): 66-67 . 百度学术
12. 江倩雯. 三电平逆变器控制方法研究. 电子元器件与信息技术. 2021(09): 80-81+86 . 百度学术
其他类型引用(17)
计量
- 文章访问数: 91
- HTML全文浏览量: 10
- PDF下载量: 15
- 被引次数: 29
下载:
