Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22543
Title: A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering
Authors: Han, F
Ashton, P
Li, M
Pisica, I
Taylor, G
Rawn, B
Ding, Y
Keywords: event detection;Kalman filtering;phasor measurement units (PMUs);random matrix theory (RMT);situational awareness
Issue Date: 13-Apr-2021
Publisher: MDPI
Citation: Han, F., Ashton, P. M., Li, M., Pisica, I., Taylor, G., Rawn, B. and Ding, Y. (2021) ‘A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering’, Energies. MDPI AG, 14(8), 2166, pp. 1-15. doi: 10.3390/en14082166.
Abstract: Copyright: © 2021 by the authors. Increasing levels of complexity, due to growing volumes of renewable generation with an associated influx of power electronics, are placing increased demands on the reliable operation of modern power systems. Consequently, phasor measurement units (PMUs) are being rapidly deployed in order to further enhance situational awareness for power system operators. This paper presents a novel data-driven event detection approach based on random matrix theory (RMT) and Kalman filtering. A dynamic Kalman filtering technique is proposed to condition PMU data. Both simulated and real PMU data from the transmission system of Great Britain (GB) are utilized in order to validate the proposed event detection approach and the results show that the proposed approach is much more robust with regard to event detection when applied in practical situations.
Description: This paper is an extended version of our paper published in 2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 5–10 August 2018.
URI: https://bura.brunel.ac.uk/handle/2438/22543
DOI: https://doi.org/10.3390/en14082166
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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