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Updated: Jul 4, 2025

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基于改进的VMD-BPNNN的剩余电流检测方法

Yunpeng Bai1, Xiangke Zhang1, Yajing Wang1

  • 1School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, Shandong, China.

PloS one
|February 8, 2024
PubMed
概括
此摘要是机器生成的。

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本研究引入了使用变量模态分解 (VMD) 和低压配电网络的BP神经网络 (BPNN) 的自适应剩余电流检测方法. 这种新的方法显著提高了检测准确度和对噪声的稳定性,为电击提供了更好的保护.

科学领域:

  • 电气工程 电气工程
  • 信号处理 信号处理
  • 人工智能的人工智能

背景情况:

  • 低压配电网络需要加强剩余电流检测,以确保安全.
  • 现有的检测方法面临着噪音和准确性方面的挑战.
  • 剩余电流检测对于防止电击至关重要.

研究的目的:

  • 提出一个改进的自适应剩余电流检测方法.
  • 为了提高低压配电网络的检测能力.
  • 为新的残流保护装置提供参考.

主要方法:

  • 结合了变量模态分解 (VMD) 和BP神经网络 (BPNN).
  • 优化VMD分解使用细菌食粒子群算法 (BFO-PSO) 与信封.
  • 采用相互关系数R和最小平均平方算法 (LMS) 来进行信号分类和过.

主要成果:

  • 实现了 16.3108dB 的信号噪声比,环境噪声为 10dB.
  • 证明了高精度,RMSE为0.4359和适合性为0.9627.
  • 在模拟和实验中表现优于VMD-LSTM和N-LMS方法.

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结论:

  • 拟议的VMD-BPNN方法提供了卓越的稳定性和检测准确性.
  • 统计分析证实了算法的高精度.
  • 该方法为开发先进的残流保护装置提供了宝贵的参考.