A Robust Spectral Clustering Method Based on PMU Measurements for Coherent Areas Identification
The paper deals with the separation of power system into coherent areas; this is a relevant issue for managing the network in both normal operating conditions and during anomalous events. In particular, the attention is focused on partitioning the power system in such a way as to group together freq...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10296893/ |
| _version_ | 1797635387008483328 |
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| author | Annalisa Liccardo Davide Lauria Francesco Bonavolonta Giorgio Maria Giannuzzi Cosimo Pisani Salvatore Tessitore |
| author_facet | Annalisa Liccardo Davide Lauria Francesco Bonavolonta Giorgio Maria Giannuzzi Cosimo Pisani Salvatore Tessitore |
| author_sort | Annalisa Liccardo |
| collection | DOAJ |
| description | The paper deals with the separation of power system into coherent areas; this is a relevant issue for managing the network in both normal operating conditions and during anomalous events. In particular, the attention is focused on partitioning the power system in such a way as to group together frequency signals, measured by means of phasor measurement units (PMU), exhibiting similar oscillatory behavior after the occurrence of a fault or disturbance. Unfortunately, the increasingly massive presence of renewable energy sources is undermining the clustering methods defined so far, requiring new solutions to the problem. To overcome the considered drawbacks, the authors propose hereinafter to (<inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>) improve the grouping capabilities of an iterative spectral clustering method thanks to the definition of new parameters for similarity estimation (Modified Bray Curtis index) and cluster thresholding (weighted Fiedler value) as well as (ii) enhance its robustness with respect to both measurement noise and uncertainty affecting the PMUs by means of a deep test procedure. To this aim, particular attention is paid in the design and assessment stage to the definition of both filtering algorithm and measurement parameters (e.g., the length of the analysis window). Once defined these parameters, the method is capable of 100% correctly separating transmission network sections oscillating with similar trends in a number of tests conducted on simulated and actual signals, so highlighting the promising performance of the method highlighting its reliability and efficacy in different test conditions. |
| first_indexed | 2024-03-11T12:21:21Z |
| format | Article |
| id | doaj.art-043cfa20f38848eb82c380b5c6cb4da9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T12:21:21Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-043cfa20f38848eb82c380b5c6cb4da92023-11-07T00:01:08ZengIEEEIEEE Access2169-35362023-01-011112144512145610.1109/ACCESS.2023.332791610296893A Robust Spectral Clustering Method Based on PMU Measurements for Coherent Areas IdentificationAnnalisa Liccardo0https://orcid.org/0000-0003-1270-4948Davide Lauria1https://orcid.org/0000-0002-8938-2599Francesco Bonavolonta2https://orcid.org/0000-0003-0666-0942Giorgio Maria Giannuzzi3https://orcid.org/0000-0003-0314-4483Cosimo Pisani4https://orcid.org/0000-0003-2964-9025Salvatore Tessitore5https://orcid.org/0000-0002-3674-9211Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, ItalyDepartment of Industrial Engineering (DII), University of Naples Federico II, Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, ItalyTerna Rete Italia, Rome, ItalyTerna Rete Italia, Rome, ItalyTerna Rete Italia, Rome, ItalyThe paper deals with the separation of power system into coherent areas; this is a relevant issue for managing the network in both normal operating conditions and during anomalous events. In particular, the attention is focused on partitioning the power system in such a way as to group together frequency signals, measured by means of phasor measurement units (PMU), exhibiting similar oscillatory behavior after the occurrence of a fault or disturbance. Unfortunately, the increasingly massive presence of renewable energy sources is undermining the clustering methods defined so far, requiring new solutions to the problem. To overcome the considered drawbacks, the authors propose hereinafter to (<inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>) improve the grouping capabilities of an iterative spectral clustering method thanks to the definition of new parameters for similarity estimation (Modified Bray Curtis index) and cluster thresholding (weighted Fiedler value) as well as (ii) enhance its robustness with respect to both measurement noise and uncertainty affecting the PMUs by means of a deep test procedure. To this aim, particular attention is paid in the design and assessment stage to the definition of both filtering algorithm and measurement parameters (e.g., the length of the analysis window). Once defined these parameters, the method is capable of 100% correctly separating transmission network sections oscillating with similar trends in a number of tests conducted on simulated and actual signals, so highlighting the promising performance of the method highlighting its reliability and efficacy in different test conditions.https://ieeexplore.ieee.org/document/10296893/Frequency oscillationsinterarea oscillationsPMU measurementspower transmission networkspectral clustering |
| spellingShingle | Annalisa Liccardo Davide Lauria Francesco Bonavolonta Giorgio Maria Giannuzzi Cosimo Pisani Salvatore Tessitore A Robust Spectral Clustering Method Based on PMU Measurements for Coherent Areas Identification IEEE Access Frequency oscillations interarea oscillations PMU measurements power transmission network spectral clustering |
| title | A Robust Spectral Clustering Method Based on PMU Measurements for Coherent Areas Identification |
| title_full | A Robust Spectral Clustering Method Based on PMU Measurements for Coherent Areas Identification |
| title_fullStr | A Robust Spectral Clustering Method Based on PMU Measurements for Coherent Areas Identification |
| title_full_unstemmed | A Robust Spectral Clustering Method Based on PMU Measurements for Coherent Areas Identification |
| title_short | A Robust Spectral Clustering Method Based on PMU Measurements for Coherent Areas Identification |
| title_sort | robust spectral clustering method based on pmu measurements for coherent areas identification |
| topic | Frequency oscillations interarea oscillations PMU measurements power transmission network spectral clustering |
| url | https://ieeexplore.ieee.org/document/10296893/ |
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