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This study introduces the Backpropagation (BP) neural network algorithm to address severe harmonic pollution in power grids caused by power electronic devices. The BP algorithm effectively analyzes harmonic components, improving detection accuracy and diagnosis speed for power system issues.

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Harmonic pollution from power electronic devices is a growing problem in power grids.
  • This pollution degrades electric energy efficiency, causes equipment overheating, and shortens line lifespan.

Purpose of the Study:

  • To analyze harmonic components in power systems effectively.
  • To introduce an advanced algorithm for rapid diagnosis and cause analysis of power system defects.

Main Methods:

  • Utilizing the Backpropagation (BP) neural network learning algorithm.
  • Establishing a mapping relationship between input and output signals to analyze harmonic frequency, amplitude, and phase.

Main Results:

  • The BP neural network algorithm demonstrated high accuracy in detecting and analyzing harmonic problems.
  • The proposed method significantly improved the speed of diagnosing defects in power system equipment.

Conclusions:

  • The BP neural network algorithm offers a robust solution for mitigating harmonic pollution in power systems.
  • This approach enhances the reliability and efficiency of power grid operations by enabling faster and more accurate fault diagnosis.