Application of random matrix model in multiple abnormal sources detection and location based on PMU monitoring data in distribution network

• Published in: IET generation, transmission & distribution Vol. 14; no. 26; pp. 6476 - 6483
• Main Authors: Yan, Yingjie, Liu, Yadong, Fang, Jian, Vijayakumar, Pandi, Sanjeevikumar, Padmanaban, Jiang, Xiuchen
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 29.12.2020
• Subjects: antinoise ability; data dimensions; economic development; energy structure; global power economy; linear shrinkage theory; Marchenko–Pastur law; matrix algebra; multiple abnormal sources detection; noise reduction; overhead transmission lines; phasor measurement; phasor measurement units; PMU monitoring data; power distribution economics; power distribution faults; power distribution lines; power distribution network disturbance; power overhead lines; power stability; power system CAD; power system inertia; power system resiliency; power system stability; PSCAD; random matrix model; random processes; renewable energy; renewable energy sources; Research Article; SCN; social development; socio‐economic effects; standard condition number rule; power distribution lines; power distribution faults; economic development; power system inertia; phasor measurement units; Marchenko–Pastur law; global power economy; overhead transmission lines; linear shrinkage theory; PSCAD; energy structure; renewable energy; noise reduction; power system CAD; multiple abnormal sources detection; socio-economic effects; power system resiliency; power system stability; renewable energy sources; antinoise ability; random processes; PMU monitoring data; power distribution economics; power stability; power distribution network disturbance; matrix algebra; random matrix model; standard condition number rule; data dimensions; power overhead lines; phasor measurement; social development; SCN
• ISSN: 1751-8687; 1751-8695; 1751-8695
• DOI: 10.1049/iet-gtd.2020.0755

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.