A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering

Fujia Han, Phillip M. Ashton, Maozhen Li, Ioana Pisica, Gareth Taylor, Barry Rawn and Yi Ding
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Fujia Han: Department of Electronic and Computer Engineering, Brunel University London, London UB8 3PH, UK
Phillip M. Ashton: Network Operations, National Grid, Wokingham RG41 5BN, UK
Maozhen Li: Department of Electronic and Computer Engineering, Brunel University London, London UB8 3PH, UK
Ioana Pisica: Brunel Institute of Power Systems, Brunel University London, London UB8 3PH, UK
Gareth Taylor: Brunel Institute of Power Systems, Brunel University London, London UB8 3PH, UK
Barry Rawn: Brunel Institute of Power Systems, Brunel University London, London UB8 3PH, UK
Yi Ding: College of Electrical Engineering, Zhejiang University, Hangzhou 310000, China

Energies, 2021, vol. 14, issue 8, 1-15

Abstract: 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.

Keywords: event detection; Kalman filtering; phasor measurement units (PMUs); random matrix theory (RMT); situational awareness (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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