Switching arc inversion based on analysis of electromagnetic characteristics

Abstract

It is difficult to describe the characteristics of an arc in low-voltage switching equipment in terms of its most essential features using conventional arc models. In this paper, arc inversion is introduced to explore a new research approach to examine the nature of low-voltage switching arcs. Based on electromagnetic analysis, the arc is equivalent to a group of threadlike current segments. Then, the arc parameters are obtained by inverting the magnetic data calculated at the sampling point array. The multi-population genetic algorithm (MPGA) is adopted to solve the Biot-Savart operator equations to search the arc position, with the current density inverted by the truncated generalized singular value decomposition (TGSVD), in which the regularization parameter is chosen by the generalized cross-validation (GCV) criterion. The result shows that, by combining the MPGA with TGSVD, both the position and current distribution of an arc can be reconstructed accurately, which can help realize arc control and guide the design of low-voltage switches with improved performance parameters. Keywords: Inversion method, MPGA, Switching arc, TGSVD