Journal
IEEE ACCESS
Volume 6, Issue -, Pages 7148-7156Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2784841
Keywords
Big data analytics; big data model; power transmission equipment; key state; large dimensional random matrix; the single ring law
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Funding
- National Natural Science Foundation of China [51477100, 61571296]
- National High Technology Research and Development Program of China (863 Program) [2015AA050204]
- China State Grid Corp Science and Technology Project
- National Science Foundation, Division of Computer and Network Systems [NSF CNS-1619250]
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This paper explores a novel idea for power equipment monitoring and finds that random matrix theory is suitable for modeling the massive data sets in this situation. Big data analytics are mined from those data. We extract the statistical correlation between key states and those parameters. In particular, the (empirical) eigenvalue spectrum distribution and the (theoretical) single ring law are derived from large-dimensional random matrices whose entries are modeled as time series. The radii of the single ring law are used as statistical analytics to characterize the measured data. The evaluation of key state and anomaly detection are accomplished through the comparison of those statistical analytics.
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