A novel superimposed voltage energy‐based approach for single phase to ground fault detection and location in distribution networks
Abstract The structure complexity and the extended geographical area are among the factors that create challenges in accurate fault location and detection in distribution networks. The single‐phase to ground (SPG) faults are considered as most frequent reasons for distribution network interruption,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-09-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12981 |
| _version_ | 1797679475448610816 |
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| author | Sajjad Miralizadeh Jalalt Sepideh Miralizadeh Vahid Talavat Tohid Ghanizadeh Boalndi |
| author_facet | Sajjad Miralizadeh Jalalt Sepideh Miralizadeh Vahid Talavat Tohid Ghanizadeh Boalndi |
| author_sort | Sajjad Miralizadeh Jalalt |
| collection | DOAJ |
| description | Abstract The structure complexity and the extended geographical area are among the factors that create challenges in accurate fault location and detection in distribution networks. The single‐phase to ground (SPG) faults are considered as most frequent reasons for distribution network interruption, which can threaten the network's reliability. Signal processing methods are usually used as common approaches to distinguish and locate faults in distribution networks. This paper proposes a novel scenario to select optimal wavelet packet transform (WPT) coefficients for SPG fault location and a method based on superimposed voltage energy to distinguish the faulty phase according to these coefficients. Furthermore, employing the energies of the superimposed voltages helps to minimize the effects of high resistance faults (HRFs) and load encroachment on the efficiency of the faulty phase detection part. Comparing the obtained results with other scenarios demonstrates the considerable efficiency of the proposed scenario. Finally, with the help of general regression neural networks (GRNN) as machine‐learning tools, a new algorithm is derived for detecting and locating the SPG fault and capacitor bank switching overvoltage (COV). Simulation results are implemented on the IEEE 34‐bus standard distribution network, demonstrating the efficiency and superiority of the suggested method. |
| first_indexed | 2024-03-11T23:15:13Z |
| format | Article |
| id | doaj.art-1f955506cea04e52a9275a5fd3e59eda |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-03-11T23:15:13Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-1f955506cea04e52a9275a5fd3e59eda2023-09-21T04:21:26ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952023-09-0117184215423310.1049/gtd2.12981A novel superimposed voltage energy‐based approach for single phase to ground fault detection and location in distribution networksSajjad Miralizadeh Jalalt0Sepideh Miralizadeh1Vahid Talavat2Tohid Ghanizadeh Boalndi3Faculty of Electrical and Computer Engineering Urmia University Urmia IranSchool of Industrial engineering Tehran University Tehran IranFaculty of Electrical and Computer Engineering Urmia University Urmia IranFaculty of Electrical and Computer Engineering Urmia University Urmia IranAbstract The structure complexity and the extended geographical area are among the factors that create challenges in accurate fault location and detection in distribution networks. The single‐phase to ground (SPG) faults are considered as most frequent reasons for distribution network interruption, which can threaten the network's reliability. Signal processing methods are usually used as common approaches to distinguish and locate faults in distribution networks. This paper proposes a novel scenario to select optimal wavelet packet transform (WPT) coefficients for SPG fault location and a method based on superimposed voltage energy to distinguish the faulty phase according to these coefficients. Furthermore, employing the energies of the superimposed voltages helps to minimize the effects of high resistance faults (HRFs) and load encroachment on the efficiency of the faulty phase detection part. Comparing the obtained results with other scenarios demonstrates the considerable efficiency of the proposed scenario. Finally, with the help of general regression neural networks (GRNN) as machine‐learning tools, a new algorithm is derived for detecting and locating the SPG fault and capacitor bank switching overvoltage (COV). Simulation results are implemented on the IEEE 34‐bus standard distribution network, demonstrating the efficiency and superiority of the suggested method.https://doi.org/10.1049/gtd2.12981distribution networksfault diagnosisfault locationwavelet transforms |
| spellingShingle | Sajjad Miralizadeh Jalalt Sepideh Miralizadeh Vahid Talavat Tohid Ghanizadeh Boalndi A novel superimposed voltage energy‐based approach for single phase to ground fault detection and location in distribution networks IET Generation, Transmission & Distribution distribution networks fault diagnosis fault location wavelet transforms |
| title | A novel superimposed voltage energy‐based approach for single phase to ground fault detection and location in distribution networks |
| title_full | A novel superimposed voltage energy‐based approach for single phase to ground fault detection and location in distribution networks |
| title_fullStr | A novel superimposed voltage energy‐based approach for single phase to ground fault detection and location in distribution networks |
| title_full_unstemmed | A novel superimposed voltage energy‐based approach for single phase to ground fault detection and location in distribution networks |
| title_short | A novel superimposed voltage energy‐based approach for single phase to ground fault detection and location in distribution networks |
| title_sort | novel superimposed voltage energy based approach for single phase to ground fault detection and location in distribution networks |
| topic | distribution networks fault diagnosis fault location wavelet transforms |
| url | https://doi.org/10.1049/gtd2.12981 |
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