Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch
查看参考文献26篇
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文摘
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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. |
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来源
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IEEE/CAA Journal of Automatica Sinica
,2017,4(2):177-185 【核心库】
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DOI
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10.1109/JAS.2017.7510496
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关键词
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Absolute average ratio process
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fault diagnosis
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microgrid inverter
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multilevel feature moving average ratio
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neural network
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地址
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College of Information Science and Engineering, Northeastern University, State Key Laboratory of Synthetical Automation for Process Industries, Shenyang, 110819
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语种
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英文 |
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文献类型
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研究性论文 |
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ISSN
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2329-9266 |
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学科
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电工技术 |
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基金
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SAPI Fundamental Research Funds
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Fundamental Research Funds for the Central Universities
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国家自然科学基金
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文献收藏号
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CSCD:5962904
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26
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