Fault Detection, Classification And Location In Power Distribution Smart Grid Using Smart Meters Data
Fault detection and location give to smart grid the ability to self-healing and isolating the fault in order to limit the negative consequences. In the literature, several techniques are proposed for detection and classification of faults using artificial intelligence algorithms. This paper proposes...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tamkang University Press
2022-05-01
|
| Series: | Journal of Applied Science and Engineering |
| Subjects: | |
| Online Access: | http://jase.tku.edu.tw/articles/jase-202301-26-1-0003 |
| _version_ | 1818239067652882432 |
|---|---|
| author | Felix Ghislain Yem Souhe Alexandre Teplaira Boum Pierre Ele Camille Franklin Mbey Vinny Junior Foba Kakeu |
| author_facet | Felix Ghislain Yem Souhe Alexandre Teplaira Boum Pierre Ele Camille Franklin Mbey Vinny Junior Foba Kakeu |
| author_sort | Felix Ghislain Yem Souhe |
| collection | DOAJ |
| description | Fault detection and location give to smart grid the ability to self-healing and isolating the fault in order to limit the negative consequences. In the literature, several techniques are proposed for detection and classification of faults using artificial intelligence algorithms. This paper proposes a novel method using fuzzy logic and neural networks for detection, classification, characterization and location of faults based on data from sensors and smart meters installed in the smart grid. The proposed technique in this paper, use simultaneously the OpenDSS-Matlab platform, makes it possible to detect and classify the fault in the network. The IEEE 37-bus system is used to verify the proposed method. The obtained precision using the proposed strategy is 99.9% which is good value in the literature. This method can be useful for network operators in detection, classification, characterization and location of faults. |
| first_indexed | 2024-12-12T12:51:39Z |
| format | Article |
| id | doaj.art-66255411e6e646d6bd5fcefd55716125 |
| institution | Directory Open Access Journal |
| issn | 2708-9967 2708-9975 |
| language | English |
| last_indexed | 2024-12-12T12:51:39Z |
| publishDate | 2022-05-01 |
| publisher | Tamkang University Press |
| record_format | Article |
| series | Journal of Applied Science and Engineering |
| spelling | doaj.art-66255411e6e646d6bd5fcefd557161252022-12-22T00:23:59ZengTamkang University PressJournal of Applied Science and Engineering2708-99672708-99752022-05-01261233410.6180/jase.202301_26(1).0003Fault Detection, Classification And Location In Power Distribution Smart Grid Using Smart Meters DataFelix Ghislain Yem Souhe0Alexandre Teplaira Boum1Pierre Ele2Camille Franklin Mbey3Vinny Junior Foba Kakeu4Department of Electrical Engineering, ENSET, University of Douala, CameroonDepartment of Electrical Engineering, ENSET, University of Douala, CameroonDepartment of Electrical Engineering, Polytechnic of Yaounde, Yaounde, CameroonDepartment of Electrical Engineering, ENSET, University of Douala, CameroonDepartment of Electrical Engineering, ENSET, University of Douala, CameroonFault detection and location give to smart grid the ability to self-healing and isolating the fault in order to limit the negative consequences. In the literature, several techniques are proposed for detection and classification of faults using artificial intelligence algorithms. This paper proposes a novel method using fuzzy logic and neural networks for detection, classification, characterization and location of faults based on data from sensors and smart meters installed in the smart grid. The proposed technique in this paper, use simultaneously the OpenDSS-Matlab platform, makes it possible to detect and classify the fault in the network. The IEEE 37-bus system is used to verify the proposed method. The obtained precision using the proposed strategy is 99.9% which is good value in the literature. This method can be useful for network operators in detection, classification, characterization and location of faults.http://jase.tku.edu.tw/articles/jase-202301-26-1-0003fault classificationfault detectionfuzzy logicsmart meter datasmart grid |
| spellingShingle | Felix Ghislain Yem Souhe Alexandre Teplaira Boum Pierre Ele Camille Franklin Mbey Vinny Junior Foba Kakeu Fault Detection, Classification And Location In Power Distribution Smart Grid Using Smart Meters Data Journal of Applied Science and Engineering fault classification fault detection fuzzy logic smart meter data smart grid |
| title | Fault Detection, Classification And Location In Power Distribution Smart Grid Using Smart Meters Data |
| title_full | Fault Detection, Classification And Location In Power Distribution Smart Grid Using Smart Meters Data |
| title_fullStr | Fault Detection, Classification And Location In Power Distribution Smart Grid Using Smart Meters Data |
| title_full_unstemmed | Fault Detection, Classification And Location In Power Distribution Smart Grid Using Smart Meters Data |
| title_short | Fault Detection, Classification And Location In Power Distribution Smart Grid Using Smart Meters Data |
| title_sort | fault detection classification and location in power distribution smart grid using smart meters data |
| topic | fault classification fault detection fuzzy logic smart meter data smart grid |
| url | http://jase.tku.edu.tw/articles/jase-202301-26-1-0003 |
| work_keys_str_mv | AT felixghislainyemsouhe faultdetectionclassificationandlocationinpowerdistributionsmartgridusingsmartmetersdata AT alexandreteplairaboum faultdetectionclassificationandlocationinpowerdistributionsmartgridusingsmartmetersdata AT pierreele faultdetectionclassificationandlocationinpowerdistributionsmartgridusingsmartmetersdata AT camillefranklinmbey faultdetectionclassificationandlocationinpowerdistributionsmartgridusingsmartmetersdata AT vinnyjuniorfobakakeu faultdetectionclassificationandlocationinpowerdistributionsmartgridusingsmartmetersdata |