The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive...

Area of Science:

• Electrical Engineering
• Signal Processing
• Materials Science

Background:

• Partial discharge (PD) is a critical factor in transformer insulation degradation.
• Noise contamination in PD signals complicates analysis and processing.
• Accurate PD signal extraction is vital for transformer health monitoring.

Purpose of the Study:

• To develop an effective denoising method for noisy transformer partial discharge signals.
• To improve the accuracy of PD signal analysis and feature extraction.
• To enhance the reliability of transformer insulation condition assessment.

Main Methods:

• Whale Optimization Algorithm (WOA) for Variational Mode Decomposition (VMD) parameter optimization.
• Kurtosis criterion for preliminary denoising of VMD mode components.
• Non-linear Minimum Mean Square (NLMS) adaptive filtering for narrowband noise removal.
• Wavelet Thresholding for residual white noise elimination.
• Proposed method: Whale VMD (WVMD) + Adaptive Filter (NLMS) + Wavelet Thresholding (WT) (WVNW).

Main Results:

• The WVNW method demonstrated superior noise suppression compared to VMD-WT and EMD-WT.
• Quantitative metrics (SNR, RMSE, NCC, NRR) confirmed the effectiveness of the proposed method.
• High waveform similarity was achieved in restoring the original PD signal.
• Preservation of significant local discharge signal features was observed.

Conclusions:

• The WVNW method offers a robust solution for denoising transformer partial discharge signals.
• This technique enhances the ability to accurately analyze and interpret PD data.
• The proposed method contributes to improved transformer insulation monitoring and maintenance strategies.