Wavelet Function Denoising Program M-File for Partial Discharge Data

This M-file program implements wavelet function denoising for partial discharge collected data. Partial discharge refers to electrical discharge phenomena caused by localized insulation defects in electrical equipment. By collecting this data, we can understand equipment health conditions and perform predictive maintenance. To enhance data quality, this program applies wavelet transform-based denoising methods to reduce noise interference. The algorithm typically involves wavelet decomposition using functions like db4 or sym8, thresholding techniques (hard or soft thresholding), and wavelet reconstruction. Key functions include wden for automated denoising or custom implementations using wavedec and waverec for multi-level decomposition. The program processes PD signals by first decomposing them into approximation and detail coefficients, then applying threshold rules to remove noise components while preserving discharge characteristics. By using this program, users obtain cleaner and more reliable data results, significantly improving equipment maintenance effectiveness and fault diagnosis accuracy through enhanced signal-to-noise ratio. The implementation includes parameter optimization for different noise levels and discharge patterns, making it adaptable to various PD measurement scenarios.