首页> 外国专利> POWER ELECTRONIC CIRCUIT TROUBLESHOOT METHOD BASED ON BEETLE ANTENNAE OPTIMIZED DEEP BELIEF NETWORK ALGORITHM

POWER ELECTRONIC CIRCUIT TROUBLESHOOT METHOD BASED ON BEETLE ANTENNAE OPTIMIZED DEEP BELIEF NETWORK ALGORITHM

机译:基于甲虫天线优化的深度信仰网络算法的电力电子电路故障排除方法

摘要

A power electronic circuit troubleshoot method based on a beetle antennae optimized deep belief network algorithm including the following steps is provided. Output current signals of DC bus of a three-phase PWM rectifier under different switching device open circuit failure modes are collected as an original data set. Intrinsic mode function components of the output current signals under different switching device open circuit failure modes are extracted using empirical mode decomposition to construct an original failure feature set. Fault feature is selected based on extra-trees to generate final fault dataset. A structure of a deep belief network is optimized using a beetle antennae algorithm. An optimized deep belief network is trained using a training set and an obtained failure recognition result is verified using a testing set.
机译:提供了基于甲虫天线的功率电子电路故障方法,提供了包括以下步骤的优化深度信念网络算法。在不同的开关装置打开电路故障模式下将三相PWM整流器DC总线的输出电流信号作为原始数据集收集。使用经验模式分解提取不同开关装置开关故障模式下的输出电流信号的固有模式功能组件,以构建原始故障特征集。基于备树选择故障功能以生成最终故障数据集。使用甲虫天线算法优化了深度信仰网络的结构。使用训练集训练优化的深度信念网络,使用测试集验证所获得的故障识别结果。

著录项

  • 公开/公告号US2021117770A1

    专利类型

  • 公开/公告日2021-04-22

    原文格式PDF

  • 申请/专利权人 WUHAN UNIVERSITY;

    申请/专利号US202015931605

  • 发明设计人 YIGANG HE;YARU ZHANG;LIULU HE;

    申请日2020-05-14

  • 分类号G06N3/08;G06N3/04;G06K9/62;G06F17/18;

  • 国家 US

  • 入库时间 2022-08-24 18:19:51

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