Particle Swarm Optimization Applied to Acoustic Emission Partial Discharge Source Localization and Its Comparison with Non-iterative Method
摘要
Condition-based maintenance (CBM) of transformers using acoustic emission partial discharge (AEPD) measurement offers notable advantages, being both non-destructive and resistant to electromagnetic interference. The precise localization of the partial discharge (PD) source is crucial, and it is achieved through an all-acoustic sensor system employing the time difference of arrival (TDOA) method. While an existing non-iterative approach ceases to localize PD sources when sensors share identical x, y, or z coordinates, the particle swarm optimization (PSO) ensures accurate localization, being an iterative method. Nevertheless, PSO struggles when relative time delays are zero in TDOA. Additionally, efforts have been made to find the best placement of acoustic sensors to enhance AEPD localization. The impact of PSO population size on PD localization efficacy is examined and reported.