Effective noise elimination is essential for ensuring data reliability in high-accuracy measurement systems. However, selecting the optimal denoising strategy for diverse and non-stationary signal types remains a major challenge. This study presents a wavelet-based denoising optimization framework that systematically identifies and applies the most suitable noise reduction model for each signal segment. By evaluating multiple wavelet types and thresholding strategies, the proposed method enables adaptive and automated selection tailored to the specific characteristics of each signal. The framework was validated using synthetic, open-access, and experimentally acquired signals in both reference-based and reference-free scenarios. Extensive testing covered signals from power quality disturbance (PQD) events, electrocardiogram (ECG) data, and electroencephalogram (EEG) recordings, all of which represent critical applications where signal integrity under noise is essential. The method achieved optimal model selection in 22.15 s (across 4558 iterations) on a standard PC, with an average denoising time of 4.86 ms per signal window. These results highlight its potential for real-time and embedded applications, including smart grid monitoring systems, wearable health devices, and automated biomedical diagnostic platforms, where adaptive, fast, and reliable denoising is vital. The framework’s versatility makes it highly relevant for deployment in smart grid monitoring systems and intelligent energy infrastructures requiring robust signal conditioning.
Sıtkı Akkaya (Mon,) studied this question.