Multiscale Adaptive Multifractal Detrended Fluctuation Analysis-Based Source Identification of Synchrophasor Data

• Published in: IEEE transactions on smart grid Vol. 13; no. 6; pp. 4957 - 4960
• Main Authors: Cui, Yi, Bai, Feifei, Yin, Hongzhi, Chen, Tong, Dart, David, Zillmann, Matthew, Ko, Ryan K. L.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-11-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer security; Cost analysis; Cyber-physical systems; cybersecurity; distribution power networks; Electric power distribution; Forestry; Fractals; Interpolation; Measuring instruments; Phasor measurement units; Phasors; PMU; Signatures; Source identification; synchrophasor; Time-frequency analysis
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2022.3207066

As a typical cyber-physical system, dispersed Phasor Measurement Units (PMUs) are networked together with advanced communication infrastructures to record the Distribution Synchrophasor (DS) which represents the states and dynamics of distribution power networks. Source information of DS is critical for many DS-based applications which is potentially vulnerable to data integrity attacks. To ensure the reliability of DS-based applications, it is imperative to efficiently authenticate the DS source locations before any DS data analytics is initiated. This letter presents a cost-effective method for accurate source identification by realising the multifractality of DS data. First, Multiscale Adaptive Multifractal Detrended Fluctuation Analysis (MSA-MFDFA) is executed to reveal the scale which possesses the most significant multifractality of the time-series DS. Subsequently, Adaptive Multifractal Interpolation (AMFI) is proposed to generate quasi high-resolution DS where unique time-frequency signatures are extracted. Such signatures are further fed into a deep learning model - deep forest for source identification. Experimental results using real-life DS of a distribution network illustrate the excellent performance of the proposed approach.