基于改进深度置信网络的中性点钳位型逆变器故障诊断方法
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新疆大学电气工程学院

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中图分类号:

TH133

基金项目:

国家自然科学基金(61963034)


Neutral point clamped inverter fault diagnosis method based on improved deep belief network
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Affiliation:

Electrical Engineering College,Xinjiang University

Fund Project:

Research on incipient fault prognosis and key technology of high power grid connected inverter wind power generation system

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    摘要:

    为解决中性点钳位型(NPC)逆变器开路故障种类较多且特征相似而造成诊断准确率不高的问题,通过自适应噪声完备经验模态分解(CEEMDAN)和改进深度置信网络(DBN)的方法研究了NPC型三电平逆变器开路故障。首先,分析了NPC型逆变器的拓扑结构和工作原理,定义不同的开路故障类型;其次,采用CEEMDAN方法和皮尔逊相关系数-能量值准则筛选敏感本征模态函数(IMF)分量,并通过希尔伯特变换求取各IMF分量包络谱,构造IMF分量和包络谱的能量和能量熵作为高维故障特征向量;最后,将多特征融合向量输入到改进哈里斯鹰算法(IHHO)优化DBN网络中完成逆变器开关器件故障辨识。通过不同仿真对比实验结果表明:采用IHHO-DBN的方法相比传统模型具有更好的诊断效果。可见本文所提方法具有一定的实际意义。

    Abstract:

    In order to solve the problem that neutral point clamped(NPC) inverters has many kinds of open faults and similar characteristics, which cause low diagnosis accuracy. CEEMDAN and improved deep belief network was used to investigate open circuit fault of three level NPC inverter. Firstly, the topology and working principle of NPC inverter are introduced and different IGBT open fault types are defined. Then CEEMDAN decomposition method and Pearson correlation-energy value criteria are combined to select sensitive IMFs, their spectrum envelopes are obtained by Hilbert transform, multiple energy and entropy of IMF components and envelope spectrums are constructed as high dimension fault characteristic vectors. Finally, multi-feature vectors are transmitted to DBN optimized by improved Harris hawk optimization algorithm. Different simulation compared experiments show that IHHO-DBN model has better diagnosis result than other traditional ones. It is concluded that there are some certain values from the proposed method in this paper.

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梁家琦,帕孜来﹒马合木提. 基于改进深度置信网络的中性点钳位型逆变器故障诊断方法[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-05-20
  • 最后修改日期:2021-08-31
  • 录用日期:2021-09-29
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