模块化五电平逆变器子模块开路故障的智能诊断方法

基于深度学习理论,提出了一种基于栈式稀疏自动编码器(SSAE)的模块化五电平逆变器(MFLI)子模块开路故障诊断方法。该方法将MFLI子模块开路故障检测与定位问题转化成分类问题,首先将子模块电容电压信号组合成24通道序列信号,然后沿着24通道序列移动大小为24×40滑动窗口获得"数据带"样本,紧接着将"数据带"转化为向量输入到SSAE中进行逐层无监督特征学习,构建原始故障数据集的深层特征简明表达,最后将深层特征简明表达连接到Softmax分类器输出故障诊断结果。此外,为了提高该方法的抗噪性能,利用已添加高斯白噪声的数据对SSAE进行训练,以提高其特征表达的鲁棒性。结果表明,所提出的故障诊断方法平均准确度达到98.09%,故障平均诊断时间为31.47ms,且具有较高的鲁棒性。

Intelligent Diagnosis Method for Open-circuit Fault of Sub-modules in Modular Five-level Inverter

Based on the deep learning theory, a novel method for sub-modular (SM) open-circuit fault diagnosis of modular five-level inverter (MFLI) is presented based on the stacked sparse auto-encoder (SSAE). The SM open-circuit fault detection and location problem of MFLI is converted into a classification problem. Firstly, the capacitor voltage signals of all SMs in the MFLI circuit are combined into a 24-channel signal. Then, by moving window along the 24-channel signal with the sliding window, a set of signal segments are acquired which are flattened into vectors and used as SSAE''s input subsequently to realize the unsupervised feature learning layer by layer. The deep feature with concise expression of original fault dataset is established and connected to the Softmax classifier to output the fault diagnostic result. In addition, in order to enhance the anti-noise performance of the proposed method, the SSAE is trained by adding Gauss noise to improve the robustness of feature expression. The results show that the proposed fault diagnosis method has the high robustness and versatility with the average accuracy of 98.09% and the average fault diagnosis time of 31.47 ms.