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Harmonic detection method based on permutation entropy and variational modal decomposition optimized by genetic

ShuQing Zhang1, Yun Zhang1

  • 1School of Electrical Engineering, Yanshan University, Qinhuangdao City, Hebei Province 066000, China.

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|March 2, 2021
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Summary
This summary is machine-generated.

This study introduces a novel harmonic detection method for power signals, using genetic algorithm-optimized variational mode decomposition (VMD) and permutation entropy. The approach effectively filters noise and enhances harmonic detection accuracy, even in low signal-to-noise environments.

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Computational Intelligence

Background:

  • Accurate harmonic component extraction is crucial for power system analysis and stability.
  • Traditional methods like empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) face challenges with noise and parameter selection.
  • Variational Mode Decomposition (VMD) offers robust decomposition but requires careful parameter tuning.

Purpose of the Study:

  • To develop an advanced harmonic detection method for power signals.
  • To improve the accuracy and noise resilience of harmonic detection in power systems.
  • To overcome the parameter selection difficulties associated with VMD.

Main Methods:

  • A novel harmonic detection technique integrating Variational Mode Decomposition (VMD) with a Genetic Algorithm (GA) for optimization.
  • Employing permutation entropy to enhance the filtering of interference signals.
  • Validating the method through simulation experiments and real-world engineering applications.

Main Results:

  • The proposed GA-optimized VMD combined with permutation entropy demonstrated superior performance compared to EMD and EEMD.
  • The method effectively filters noise, particularly in low signal-to-noise ratio (SNR) conditions.
  • Significantly higher harmonic detection accuracy was achieved.

Conclusions:

  • The proposed method offers an effective and accurate solution for harmonic detection in power systems.
  • GA optimization addresses VMD's parameter selection challenges, improving its practical applicability.
  • The integration with permutation entropy enhances noise suppression and overall detection reliability.