电力系统自动化2018，Vol.42Issue(12)：127-133,147,8.DOI:10.7500/AEPS20170714008
模块化五电平逆变器子模块开路故障的智能诊断方法
Intelligent Diagnosis Method for Open-circuit Fault of Sub-modules in Modular Five-level Inverter
摘要
Abstract
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.关键词
模块化五电平逆变器/无监督特征学习/栈式稀疏自动编码器/智能诊断/开路故障Key words
modular five-level inverter (MFLI)/unsupervised feature learning/stacked sparse auto-encoder (SSAE)/intelligent diagnosis/open-circuit fault引用本文复制引用
尹桥宣,段斌,沈梦君,屈相帅..模块化五电平逆变器子模块开路故障的智能诊断方法[J].电力系统自动化,2018,42(12):127-133,147,8.基金项目
国家自然科学基金资助项目(61379063).This work is supported by National Natural Science Foundation of China(No.61379063). （61379063）
