| Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch | |
| Alternative Title | Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch |
| Huang Zhanjun1; Wang Zhanshan1; Zhang Huaguang1 | |
| 2017 | |
| Source Publication | IEEE-CAA JOURNAL OF AUTOMATICA SINICA
 |
| ISSN | 2329-9266 |
| Volume | 4Issue:2Pages:177-185 |
| Abstract | Multilevel feature moving average ratio method is proposed to realize an open-switch fault diagnosis for any switch of the microgrid inverter. The main steps of the proposed method include multilevel signal decomposition, coefficient reconstruction, absolute average ratio process and artificial neural network (ANN) classification. Specifically, multilevel signal decomposition is realized by using the means of multi resolution analysis to obtain the different frequency band coefficients of the three-phase current signal. The related coefficient reconstruction is executed to achieve signals decomposition in different levels. Furthermore, according to the obtained data, the absolute average ratio process is used to extract absolute moving average ratio of signal decomposition in different levels for the three-phase current. Finally, to intelligently classify the inverter switch fault and realize the adaptive ability, the ANN technology is applied. Compared to conventional fault diagnosis methods, the proposed method can accurately detect and locate the open-switch fault for any location of the microgrid inverter. Additionally, it need not set related threshold of algorithm and does not require normalization process, which is relatively easy to implement. The effectiveness of the proposed fault diagnosis method is demonstrated through detailed simulation results. |
| Other Abstract | Multilevel feature moving average ratio method is proposed to realize an open-switch fault diagnosis for any switch of the microgrid inverter. The main steps of the proposed method include multilevel signal decomposition, coefficient reconstruction, absolute average ratio process and artificial neural network (ANN) classification. Specifically, multilevel signal decomposition is realized by using the means of multi resolution analysis to obtain the different frequency band coefficients of the three-phase current signal. The related coefficient reconstruction is executed to achieve signals decomposition in different levels. Furthermore, according to the obtained data, the absolute average ratio process is used to extract absolute moving average ratio of signal decomposition in different levels for the three-phase current. Finally, to intelligently classify the inverter switch fault and realize the adaptive ability, the ANN technology is applied. Compared to conventional fault diagnosis methods, the proposed method can accurately detect and locate the open-switch fault for any location of the microgrid inverter. Additionally, it need not set related threshold of algorithm and does not require normalization process, which is relatively easy to implement. The effectiveness of the proposed fault diagnosis method is demonstrated through detailed simulation results. |
| Keyword | ARTIFICIAL NEURAL NETWORKS WIND TURBINE SYSTEMS OPEN-CIRCUIT FAULT POWER-SYSTEMS DRIVE SYSTEMS 2-LEVEL PWM Absolute average ratio process fault diagnosis microgrid inverter multilevel feature moving average ratio neural network |
| Indexed By | CSCD |
| Language | 英语 |
| Funding Project | [National Natural Science Foundation of China] ; [Fundamental Research Funds for the Central Universities] ; [SAPI Fundamental Research Funds] |
| CSCD ID | CSCD:5962904 |
| Citation statistics |
Cited Times:3[CSCD]
[CSCD Record]
|
| Document Type | 期刊论文 |
| Identifier | http://ir.imr.ac.cn/handle/321006/141754 |
| Collection | 中国科学院金属研究所 |
| Affiliation | 1.东北大学 2.中国科学院金属研究所 |
| Recommended Citation GB/T 7714 | Huang Zhanjun,Wang Zhanshan,Zhang Huaguang. Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch[J]. IEEE-CAA JOURNAL OF AUTOMATICA SINICA,2017,4(2):177-185. |
| APA | Huang Zhanjun,Wang Zhanshan,&Zhang Huaguang.(2017).Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch.IEEE-CAA JOURNAL OF AUTOMATICA SINICA,4(2),177-185. |
| MLA | Huang Zhanjun,et al."Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch".IEEE-CAA JOURNAL OF AUTOMATICA SINICA 4.2(2017):177-185. |
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