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Residual current detection method based on improved VMD-BPNN.

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
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This study introduces an adaptive residual current detection method using variational modal decomposition (VMD) and a BP neural network (BPNN) for low-voltage distribution networks. The novel approach significantly improves detection accuracy and robustness against noise, offering enhanced protection against electric shock.

Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Low-voltage distribution networks require enhanced residual current detection for safety.
  • Existing detection methods face challenges with noise and accuracy.
  • Residual current detection is critical for preventing electric shock.

Purpose of the Study:

  • To propose an improved adaptive residual current detection method.
  • To enhance the detection capability in low-voltage distribution networks.
  • To provide a reference for new residual current protection devices.

Main Methods:

  • Combines variational modal decomposition (VMD) and BP neural network (BPNN).
  • Optimizes VMD decomposition using bacterial foraging-particle swarm algorithm (BFO-PSO) with envelope entropy.

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  • Employs interrelation number R and Least Mean Square Algorithm (LMS) for signal classification and filtering.

    • Main Results:

    • Achieved a signal-to-noise ratio of 16.3108dB with 10dB ambient noise.
    • Demonstrated high accuracy with RMSE of 0.4359 and goodness-of-fit of 0.9627.
    • Outperformed VMD-LSTM and N-LMS methods in simulations and experiments.

    Conclusions:

    • The proposed VMD-BPNN method offers superior robustness and detection accuracy.
    • Statistical analysis confirmed the algorithm's high precision.
    • The method provides a valuable reference for developing advanced residual current protection devices.