Data‐driven power system linear model identification for selective modal analysis by frequency interpolations
Abstract This paper proposes a new approach to identify a data‐based power system linear model by means of frequency interpolations, aiming to obtain a suitable system representation for selective‐modal analysis purposes. The key idea behind the identification process is the Loewner‐based frequency...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-03-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12084 |
| _version_ | 1828231087102361600 |
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| author | Francisco Zelaya‐A. Joe H. Chow Mario. R. Arrieta Paternina Alejandro Zamora‐Mendez |
| author_facet | Francisco Zelaya‐A. Joe H. Chow Mario. R. Arrieta Paternina Alejandro Zamora‐Mendez |
| author_sort | Francisco Zelaya‐A. |
| collection | DOAJ |
| description | Abstract This paper proposes a new approach to identify a data‐based power system linear model by means of frequency interpolations, aiming to obtain a suitable system representation for selective‐modal analysis purposes. The key idea behind the identification process is the Loewner‐based frequency interpolation carried out by the Loewner matrices. The proposed approach demonstrates that the Loewner‐based frequency interpolation is able to fit a linear model that can be used for small‐signal analysis studies, since it provides the state‐space representation, the frequency response, and modal information (frequency, damping, and mode‐shape). Then, a selective modal analysis is accomplished over two test cases (Kundur and New England–New York power grids) by employing the identified linear model provided by the Loewner‐based frequency interpolation method. The attained results confirm the outstanding performance of the proposal which is validated against the small‐signal analysis and compared with the eigensystem realisation algorithm, overcoming the absolute error of the model identified with the traditional eigensystem realisation algorithm approach by at least 37 times, and properly capturing the modal information in a frequency band of concern. |
| first_indexed | 2024-04-12T18:58:09Z |
| format | Article |
| id | doaj.art-e25f7c8ee6d64780b957688e26c4c71c |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-12T18:58:09Z |
| publishDate | 2021-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-e25f7c8ee6d64780b957688e26c4c71c2022-12-22T03:20:15ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952021-03-011561107112110.1049/gtd2.12084Data‐driven power system linear model identification for selective modal analysis by frequency interpolationsFrancisco Zelaya‐A.0Joe H. Chow1Mario. R. Arrieta Paternina2Alejandro Zamora‐Mendez3National Autonomous University of Mexico (UNAM) Mex. 04510 MexicoRensselaer Polytechnic Institute Troy New York USANational Autonomous University of Mexico (UNAM) Mex. 04510 MexicoMichoacan University of Saint Nicholas of Hidalgo (UMSNH) Morelia Michoacan MexicoAbstract This paper proposes a new approach to identify a data‐based power system linear model by means of frequency interpolations, aiming to obtain a suitable system representation for selective‐modal analysis purposes. The key idea behind the identification process is the Loewner‐based frequency interpolation carried out by the Loewner matrices. The proposed approach demonstrates that the Loewner‐based frequency interpolation is able to fit a linear model that can be used for small‐signal analysis studies, since it provides the state‐space representation, the frequency response, and modal information (frequency, damping, and mode‐shape). Then, a selective modal analysis is accomplished over two test cases (Kundur and New England–New York power grids) by employing the identified linear model provided by the Loewner‐based frequency interpolation method. The attained results confirm the outstanding performance of the proposal which is validated against the small‐signal analysis and compared with the eigensystem realisation algorithm, overcoming the absolute error of the model identified with the traditional eigensystem realisation algorithm approach by at least 37 times, and properly capturing the modal information in a frequency band of concern.https://doi.org/10.1049/gtd2.12084Interpolation and function approximation (numerical analysis)Linear algebra (numerical analysis)Power systems |
| spellingShingle | Francisco Zelaya‐A. Joe H. Chow Mario. R. Arrieta Paternina Alejandro Zamora‐Mendez Data‐driven power system linear model identification for selective modal analysis by frequency interpolations IET Generation, Transmission & Distribution Interpolation and function approximation (numerical analysis) Linear algebra (numerical analysis) Power systems |
| title | Data‐driven power system linear model identification for selective modal analysis by frequency interpolations |
| title_full | Data‐driven power system linear model identification for selective modal analysis by frequency interpolations |
| title_fullStr | Data‐driven power system linear model identification for selective modal analysis by frequency interpolations |
| title_full_unstemmed | Data‐driven power system linear model identification for selective modal analysis by frequency interpolations |
| title_short | Data‐driven power system linear model identification for selective modal analysis by frequency interpolations |
| title_sort | data driven power system linear model identification for selective modal analysis by frequency interpolations |
| topic | Interpolation and function approximation (numerical analysis) Linear algebra (numerical analysis) Power systems |
| url | https://doi.org/10.1049/gtd2.12084 |
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