This paper proposes a fingerprint matching method integrating transfer learning and online learning to tackle the challenges of environmental adaptability and dynamic interference resistance in photovoltaic (PV) array DC arc fault location methods based on electromagnetic radiation (EMR)signals.Initially

a comprehensive analysis of the time-frequency characteristics of series arc EMR signals is carried out to pinpoint effective data sources that reflect fault features.Subsequently

a multi-kernel domain-adversarial neural network(MKDANN)is introduced to extract domain-invariant features

and a feature extractor designed specifically for fingerprint matching is devised.To reduce inter-domain distribution differences

a multi-kernel maximum mean discrepancy (MK-MMD) is integrated into the adaptation layer.Moreover

to deal with dynamic environmental changes in real-world situations

the support-class passive aggressive(SPA) algorithm is utilized to adjust model parameters in real time.Finally

MKDANN and SPA technologies are smoothly combined to build a fully operational fault location model.Experimental results indicate that the proposed method attains an overall fault location accuracy of at least 95%

showing strong adaptability to environmental changes and robust interference resistance while maintaining excellent online learning capabilities during model migration.