A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering
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 furthe...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/8/2166 |
| _version_ | 1797537894502498304 |
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| author | Fujia Han Phillip M. Ashton Maozhen Li Ioana Pisica Gareth Taylor Barry Rawn Yi Ding |
| author_facet | Fujia Han Phillip M. Ashton Maozhen Li Ioana Pisica Gareth Taylor Barry Rawn Yi Ding |
| author_sort | Fujia Han |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:22:44Z |
| format | Article |
| id | doaj.art-f484368eddec45e692dd8dd49e75b472 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T12:22:44Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f484368eddec45e692dd8dd49e75b4722023-11-21T15:23:16ZengMDPI AGEnergies1996-10732021-04-01148216610.3390/en14082166A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman FilteringFujia Han0Phillip M. Ashton1Maozhen Li2Ioana Pisica3Gareth Taylor4Barry Rawn5Yi Ding6Department of Electronic and Computer Engineering, Brunel University London, London UB8 3PH, UKNetwork Operations, National Grid, Wokingham RG41 5BN, UKDepartment of Electronic and Computer Engineering, Brunel University London, London UB8 3PH, UKBrunel Institute of Power Systems, Brunel University London, London UB8 3PH, UKBrunel Institute of Power Systems, Brunel University London, London UB8 3PH, UKBrunel Institute of Power Systems, Brunel University London, London UB8 3PH, UKCollege of Electrical Engineering, Zhejiang University, Hangzhou 310000, ChinaIncreasing 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.https://www.mdpi.com/1996-1073/14/8/2166event detectionKalman filteringphasor measurement units (PMUs)random matrix theory (RMT)situational awareness |
| spellingShingle | Fujia Han Phillip M. Ashton Maozhen Li Ioana Pisica Gareth Taylor Barry Rawn Yi Ding A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering Energies event detection Kalman filtering phasor measurement units (PMUs) random matrix theory (RMT) situational awareness |
| title | A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering |
| title_full | A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering |
| title_fullStr | A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering |
| title_full_unstemmed | A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering |
| title_short | A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering |
| title_sort | data driven approach to robust event detection in smart grids based on random matrix theory and kalman filtering |
| topic | event detection Kalman filtering phasor measurement units (PMUs) random matrix theory (RMT) situational awareness |
| url | https://www.mdpi.com/1996-1073/14/8/2166 |
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