Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and Naive Bayes classifier
This paper presents the methodology to detect and identify the type of fault that occurs in the shunt compensated static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes (NB) classifiers. To study this, the network model is d...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/38182/ http://psasir.upm.edu.my/id/eprint/38182/1/38182.pdf |
| _version_ | 1848848808098660352 |
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| author | Aker, Elhadi Emhemad Othman, Mohammad Lutfi Veerasamy, Veerapandiyan Aris, Ishak Abdul Wahab, Noor Izzri Hizam, Hashim |
| author_facet | Aker, Elhadi Emhemad Othman, Mohammad Lutfi Veerasamy, Veerapandiyan Aris, Ishak Abdul Wahab, Noor Izzri Hizam, Hashim |
| author_sort | Aker, Elhadi Emhemad |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents the methodology to detect and identify the type of fault that occurs in the shunt compensated static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes (NB) classifiers. To study this, the network model is designed using Matlab/Simulink. Different types of faults, such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and the three-phase (LLLG) fault, are applied at disparate zones of the system, with and without STATCOM, considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using Daubechies (db) mother wavelet of db4 to extract the features, such as the standard deviation (SD) and energy values. Then, the extracted features are used to train the classifiers, such as Multi-Layer Perceptron Neural Network (MLP), Bayes and the Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results obtained reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), percentage relative absolute error (% RAE) and percentage root relative square error (% RRSE) than both MLP and the Bayes classifier. |
| first_indexed | 2025-11-15T09:40:23Z |
| format | Article |
| id | upm-38182 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:40:23Z |
| publishDate | 2020 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-381822020-05-03T23:04:08Z http://psasir.upm.edu.my/id/eprint/38182/ Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and Naive Bayes classifier Aker, Elhadi Emhemad Othman, Mohammad Lutfi Veerasamy, Veerapandiyan Aris, Ishak Abdul Wahab, Noor Izzri Hizam, Hashim This paper presents the methodology to detect and identify the type of fault that occurs in the shunt compensated static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes (NB) classifiers. To study this, the network model is designed using Matlab/Simulink. Different types of faults, such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and the three-phase (LLLG) fault, are applied at disparate zones of the system, with and without STATCOM, considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using Daubechies (db) mother wavelet of db4 to extract the features, such as the standard deviation (SD) and energy values. Then, the extracted features are used to train the classifiers, such as Multi-Layer Perceptron Neural Network (MLP), Bayes and the Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results obtained reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), percentage relative absolute error (% RAE) and percentage root relative square error (% RRSE) than both MLP and the Bayes classifier. MDPI 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/38182/1/38182.pdf Aker, Elhadi Emhemad and Othman, Mohammad Lutfi and Veerasamy, Veerapandiyan and Aris, Ishak and Abdul Wahab, Noor Izzri and Hizam, Hashim (2020) Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and Naive Bayes classifier. Energies, 13 (1). art. no. 243. pp. 1-24. ISSN 1996-1073 https://www.mdpi.com/1996-1073/13/1/243 10.3390/en13010243 |
| spellingShingle | Aker, Elhadi Emhemad Othman, Mohammad Lutfi Veerasamy, Veerapandiyan Aris, Ishak Abdul Wahab, Noor Izzri Hizam, Hashim Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and Naive Bayes classifier |
| title | Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and Naive Bayes classifier |
| title_full | Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and Naive Bayes classifier |
| title_fullStr | Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and Naive Bayes classifier |
| title_full_unstemmed | Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and Naive Bayes classifier |
| title_short | Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and Naive Bayes classifier |
| title_sort | fault detection and classification of shunt compensated transmission line using discrete wavelet transform and naive bayes classifier |
| url | http://psasir.upm.edu.my/id/eprint/38182/ http://psasir.upm.edu.my/id/eprint/38182/ http://psasir.upm.edu.my/id/eprint/38182/ http://psasir.upm.edu.my/id/eprint/38182/1/38182.pdf |