Partial discharge (PD) source localization is an essential technology to identify the location of defects in power cables. This paper presents a complete cable PD localization system. To improve localization accuracy and reduce computational cost, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise—Multiscale Permutation Entropy–Improved Wavelet Threshold (CEEMDAN-MPE-IWT) method is first employed to effectively suppress noise in PD signals. Subsequently, Cross-Correlation (CC) coefficients are calculated between the double-ended signals to eliminate low-quality signals with poor correlation. Furthermore, the retained signals are subjected to time-window cropping to minimize redundant data and enhance computational efficiency. Based on the processed signals, multiple time delay estimates are derived using the Generalized Cross-Correlation (GCC) algorithm, and the K-means clustering algorithm is subsequently applied to determine the final localization result. Finally, a cable PD experimental platform is established to validate the proposed method. Experimental results demonstrate that the proposed approach achieves a relative localization error of less than 3%, indicating high localization accuracy and strong potential for engineering applications.
Zhu et al. (Sun,) studied this question.