High impedance fault detection in medium voltage distribution network using discrete wavelet transform and adaptive neuro-fuzzy inference system
This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/74504/ http://psasir.upm.edu.my/id/eprint/74504/1/High%20impedance%20fault%20detection%20in%20medium%20voltage%20distribution%20network%20using%20discrete%20wavelet%20transform%20and%20adaptive%20neuro-fuzzy%20inference%20system.pdf |
| _version_ | 1848857509992857600 |
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| author | Veerasamy, Veerapandiyan Abdul Wahab, Noor Izzri Ramachandran, Rajeswari Mansoor, Muhammad Thirumeni, Mariammal |
| author_facet | Veerasamy, Veerapandiyan Abdul Wahab, Noor Izzri Ramachandran, Rajeswari Mansoor, Muhammad Thirumeni, Mariammal |
| author_sort | Veerasamy, Veerapandiyan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system. |
| first_indexed | 2025-11-15T11:58:41Z |
| format | Article |
| id | upm-74504 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:58:41Z |
| publishDate | 2018 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-745042020-10-17T21:21:02Z http://psasir.upm.edu.my/id/eprint/74504/ High impedance fault detection in medium voltage distribution network using discrete wavelet transform and adaptive neuro-fuzzy inference system Veerasamy, Veerapandiyan Abdul Wahab, Noor Izzri Ramachandran, Rajeswari Mansoor, Muhammad Thirumeni, Mariammal This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system. MDPI 2018-10 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/74504/1/High%20impedance%20fault%20detection%20in%20medium%20voltage%20distribution%20network%20using%20discrete%20wavelet%20transform%20and%20adaptive%20neuro-fuzzy%20inference%20system.pdf Veerasamy, Veerapandiyan and Abdul Wahab, Noor Izzri and Ramachandran, Rajeswari and Mansoor, Muhammad and Thirumeni, Mariammal (2018) High impedance fault detection in medium voltage distribution network using discrete wavelet transform and adaptive neuro-fuzzy inference system. Energies, 11 (12). 1(24)-24(24). ISSN 1996-1073 10.20944/preprints201810.0687.v1 |
| spellingShingle | Veerasamy, Veerapandiyan Abdul Wahab, Noor Izzri Ramachandran, Rajeswari Mansoor, Muhammad Thirumeni, Mariammal High impedance fault detection in medium voltage distribution network using discrete wavelet transform and adaptive neuro-fuzzy inference system |
| title | High impedance fault detection in medium voltage distribution network using discrete wavelet transform and adaptive neuro-fuzzy inference system |
| title_full | High impedance fault detection in medium voltage distribution network using discrete wavelet transform and adaptive neuro-fuzzy inference system |
| title_fullStr | High impedance fault detection in medium voltage distribution network using discrete wavelet transform and adaptive neuro-fuzzy inference system |
| title_full_unstemmed | High impedance fault detection in medium voltage distribution network using discrete wavelet transform and adaptive neuro-fuzzy inference system |
| title_short | High impedance fault detection in medium voltage distribution network using discrete wavelet transform and adaptive neuro-fuzzy inference system |
| title_sort | high impedance fault detection in medium voltage distribution network using discrete wavelet transform and adaptive neuro-fuzzy inference system |
| url | http://psasir.upm.edu.my/id/eprint/74504/ http://psasir.upm.edu.my/id/eprint/74504/ http://psasir.upm.edu.my/id/eprint/74504/1/High%20impedance%20fault%20detection%20in%20medium%20voltage%20distribution%20network%20using%20discrete%20wavelet%20transform%20and%20adaptive%20neuro-fuzzy%20inference%20system.pdf |