Dual-Stage Optimization for Cable Fault Localization Wavelet Adaptive Denoising and PSO-GA Hybrid Windowing

To address the challenges of “pseudo-peak” misjudgment rates in broadband impedance spectroscopy-based cable fault localization, this paper proposes a dual-stage optimization method integrating wavelet adaptive denoising with particle swarm-genetic hybrid windowing. The study first introduces a wavelet adaptive denoising algorithm based on multi-scale noise variance dynamic estimation, which combines the Garrote threshold function to directionally filter high-frequency noise. This approach preserves impedance mutation features while significantly enhancing the signal-to-noise ratio and reducing mean square error. Subsequently, a PSO-GA hybrid intelligent windowing algorithm is designed, dynamically optimizing the Kaiser window’s main lobe width and side lobe attenuation factor by integrating the rapid convergence of particle swarm optimization with the global search capability of genetic algorithms, thereby overcoming the limitations of traditional window functions in high-frequency resolution. Simulations and experiments demonstrate that the proposed method improves the localization peak by three times and optimizes localization accuracy to one-third of the original in single-defect scenarios, achieving optimal mean square error and signal-to-noise ratio. In multi-defect scenarios, all metrics outperform traditional algorithms, significantly enhancing robustness and localization accuracy in complex noise environments.