Aiming at the issue of arc fault detection and localization in photovoltaic arrays, particularly in large-scale photovoltaic systems, this study constructs equivalent circuit models of arc faults under diverse operational scenarios to analyze voltage and current characteristic differences induced by arc faults of distinct locations and varying types. Based on the typical fault characteristics observed in Photovoltaic string-side operations, this study establishes a detection criterion integrating characteristic frequency band energy ratios and differences between the mean values. Furthermore, leveraging the distinct fault signatures of busbar-side faults, a detection criterion combining voltage mean values and differences from the means is established. Finally, by synergistically utilizing multivariate information from both string currents and busbar voltages, an IoT-based strategy for direct current arc fault detection and localization in photovoltaic systems is proposed. The method proposed in this paper not only achieves fault detection but also accurately identifies fault types and determines the fault segments. This method exhibits good anti-interference capabilities, effectively distinguishing between non-fault conditions such as shadow occlusion. Experimental results demonstrate that this method significantly outperforms traditional single-feature detection methods. It represents a novel approach to implementing arc fault detection through Internet of Things technologies.