Improving protection reliability of series‐compensated transmission lines by a fault detection method through an ML‐based model
Abstract This article addresses the distance protection challenges associated with the series‐compensated transmission lines and the impact of fault resistance by employing a machine‐learning model. In the proposed model, stacked layers of bidirectional long short‐term memory (Bi‐LSTM) cells are fed...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | IET Generation, Transmission & Distribution |
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| Online Access: | https://doi.org/10.1049/gtd2.13294 |
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| _version_ | 1846170922573103104 |
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| author | Hossein Ebrahimi Sajjad Golshannavaz Amin Yazdaninejadi Edris Pouresmaeil |
| author_facet | Hossein Ebrahimi Sajjad Golshannavaz Amin Yazdaninejadi Edris Pouresmaeil |
| author_sort | Hossein Ebrahimi |
| collection | DOAJ |
| description | Abstract This article addresses the distance protection challenges associated with the series‐compensated transmission lines and the impact of fault resistance by employing a machine‐learning model. In the proposed model, stacked layers of bidirectional long short‐term memory (Bi‐LSTM) cells are fed by voltage and current signals to distinguish between different fault scenarios. This method takes advantage of only local bus measurements to prevent information leakage in communication channels. Moreover, to make the proposed method harmonics‐robust and improve the correlation interpretation between the features for the Bi‐LSTM model, the 3‐phase raw measurement signals are passed through a discrete Fourier transform (DFT) which extracts their fundamental frequency component magnitudes and angles. Then, an extensive amount of fault scenarios including different compensation levels, fault resistances, and fault locations in normal and power‐swing operational conditions are simulated to train the model. Finally, to validate the performance of the proposed protection method in the series‐compensated transmission lines, distinctive studies are also carried out based on electromagnetic transient simulations. The obtained results confirm the remarkable performance of the proposed method in discriminating fault types, faulty phases, internal or external faults, and normal or power‐swing conditions of the power system. |
| format | Article |
| id | doaj-art-a6a755f9eb3e44a7bcd37a30a72229b9 |
| institution | Kabale University |
| issn | 1751-8687 1751-8695 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj-art-a6a755f9eb3e44a7bcd37a30a72229b92024-11-11T11:03:12ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952024-11-0118213452346110.1049/gtd2.13294Improving protection reliability of series‐compensated transmission lines by a fault detection method through an ML‐based modelHossein Ebrahimi0Sajjad Golshannavaz1Amin Yazdaninejadi2Edris Pouresmaeil3Electrical Engineering Department Urmia University Urmia IranElectrical Engineering Department Urmia University Urmia IranElectrical Engineering Department Shahid Rajaee Teacher Training University Tehran IranElectrical Engineering Department Aalto University Espoo FinlandAbstract This article addresses the distance protection challenges associated with the series‐compensated transmission lines and the impact of fault resistance by employing a machine‐learning model. In the proposed model, stacked layers of bidirectional long short‐term memory (Bi‐LSTM) cells are fed by voltage and current signals to distinguish between different fault scenarios. This method takes advantage of only local bus measurements to prevent information leakage in communication channels. Moreover, to make the proposed method harmonics‐robust and improve the correlation interpretation between the features for the Bi‐LSTM model, the 3‐phase raw measurement signals are passed through a discrete Fourier transform (DFT) which extracts their fundamental frequency component magnitudes and angles. Then, an extensive amount of fault scenarios including different compensation levels, fault resistances, and fault locations in normal and power‐swing operational conditions are simulated to train the model. Finally, to validate the performance of the proposed protection method in the series‐compensated transmission lines, distinctive studies are also carried out based on electromagnetic transient simulations. The obtained results confirm the remarkable performance of the proposed method in discriminating fault types, faulty phases, internal or external faults, and normal or power‐swing conditions of the power system.https://doi.org/10.1049/gtd2.13294artificial intelligencedistribution planning and operationpower system protection |
| spellingShingle | Hossein Ebrahimi Sajjad Golshannavaz Amin Yazdaninejadi Edris Pouresmaeil Improving protection reliability of series‐compensated transmission lines by a fault detection method through an ML‐based model IET Generation, Transmission & Distribution artificial intelligence distribution planning and operation power system protection |
| title | Improving protection reliability of series‐compensated transmission lines by a fault detection method through an ML‐based model |
| title_full | Improving protection reliability of series‐compensated transmission lines by a fault detection method through an ML‐based model |
| title_fullStr | Improving protection reliability of series‐compensated transmission lines by a fault detection method through an ML‐based model |
| title_full_unstemmed | Improving protection reliability of series‐compensated transmission lines by a fault detection method through an ML‐based model |
| title_short | Improving protection reliability of series‐compensated transmission lines by a fault detection method through an ML‐based model |
| title_sort | improving protection reliability of series compensated transmission lines by a fault detection method through an ml based model |
| topic | artificial intelligence distribution planning and operation power system protection |
| url | https://doi.org/10.1049/gtd2.13294 |
| work_keys_str_mv | AT hosseinebrahimi improvingprotectionreliabilityofseriescompensatedtransmissionlinesbyafaultdetectionmethodthroughanmlbasedmodel AT sajjadgolshannavaz improvingprotectionreliabilityofseriescompensatedtransmissionlinesbyafaultdetectionmethodthroughanmlbasedmodel AT aminyazdaninejadi improvingprotectionreliabilityofseriescompensatedtransmissionlinesbyafaultdetectionmethodthroughanmlbasedmodel AT edrispouresmaeil improvingprotectionreliabilityofseriescompensatedtransmissionlinesbyafaultdetectionmethodthroughanmlbasedmodel |