Abstract
Electric Network Frequency (ENF) signals are the signatures of power systems that are either directly recorded from the power outlets or extracted from multimedia recordings near the electrical activities. Variations of ENF signals collected at different locations possess local environmental characteristics, which can be used as a potential fingerprint for authenticating measurements' source information. Within this paper is proposed a computational intelligence-based framework to recognize the source locations of power ENF signals within a distribution network in the US. To be more specific, a set of informative location-sensitive signatures from ENF measurements are initially extract with such measurements representative of local grid characteristics. Then these distinctive location-dependent signatures are further fed into a data mining algorithm yielding the source-of-origin of ENF measurements. Experimental results using ENF data at multiple intra-grid locations have validated the proposed methodology.
