Open AccessApplication of random matrix model in multiple abnormal sources detection and location based on PMU monitoring data in distribution network
Author(s) -
Yan Yingjie,
Liu Yadong,
Fang Jian,
Vijayakumar Pandi,
Sanjeevikumar Padmanaban,
Jiang Xiuchen
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.0755
Subject(s) - overhead (engineering) , phasor measurement unit , phasor , noise (video) , computer science , renewable energy , stability (learning theory) , distributed generation , network topology , electric power system , power (physics) , topology (electrical circuits) , control theory (sociology) , engineering , computer network , artificial intelligence , electrical engineering , image (mathematics) , operating system , control (management) , physics , quantum mechanics , machine learning
With the conversion of the global power economy and energy structure, access to a large amount of renewable energy has led to a decrease in power system inertia. The slight abnormal disturbance in the distribution network may have a significant impact on social and economic development. Aim at enhancing power stability and system resiliency; this study focuses on the detection and location of multiple abnormal sources in the distribution network. Most traditional methods use models relying on precise line parameters, subject to poor adaptability to the distribution network with a large number of nodes, and rapidly changing topology. Therefore, this study proposes a novel random matrix model, driven by monitoring data from phasor measurement units distributed on the overhead transmission lines. In this model, linear shrinkage (LS) theory, and Marchenko–Pastur law are combined for noise reduction to ensure the dynamic character and anti‐noise ability. Moreover, data dimensions and sample points may be at the same level in an extensive scale network. The LS and standard condition number rule (SCN) are used for estimating the number of abnormal sources. Finally, the effectiveness of this paper's model is verified in PSCAD. The results indicate that the method has specific dynamic performance and anti‐noise ability.