Performance analysis of intelligent classifiers for high impedance fault detection in a PV-integrated IEEE-13 bus system
High impedance faults (HIFs) present significant challenges in power systems, particularly when an electrical wire contacts a high-resistance material, leading to low currents that are difficult for traditional relays to detect. With the increasing integration of photovoltaic (PV) systems, these cha...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE Canada
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/118346/ http://psasir.upm.edu.my/id/eprint/118346/1/118346.pdf |
| _version_ | 1848867494807207936 |
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| author | Mahzan, Najwa Nasuha Lutfi Othman, Mohammad Izzri Abdul Wahab, Noor Veerasamy, Veerapandiyan Salim, Nur Ashida Azwin Zainul Abidin, Aidil Zahurul Islam, Syed |
| author_facet | Mahzan, Najwa Nasuha Lutfi Othman, Mohammad Izzri Abdul Wahab, Noor Veerasamy, Veerapandiyan Salim, Nur Ashida Azwin Zainul Abidin, Aidil Zahurul Islam, Syed |
| author_sort | Mahzan, Najwa Nasuha |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | High impedance faults (HIFs) present significant challenges in power systems, particularly when an electrical wire contacts a high-resistance material, leading to low currents that are difficult for traditional relays to detect. With the increasing integration of photovoltaic (PV) systems, these challenges are exacerbated due to the complex behavior of PV-generated signals. This study aims to enhance the detection of HIFs in PV-integrated systems using advanced machine learning techniques. The approach employs various classifiers including artificial neural networks, support vector machines, decision trees, and Random Forest to improve fault identification accuracy. A MATLAB/SIMULINK simulation was conducted on an IEEE 13-bus system with a 300 kW solar PV plant. The Discrete Wavelet Transform (DWT) with the db4 wavelet was used for feature extraction, focusing on phase energy values. The classifiers were evaluated under different scenarios such as normal operation, load switching, capacitor switching, HIF, and line-to-ground (LG) faults. The Random Forest classifier outperformed others, achieving a fault detection accuracy of 99.4083%, demonstrating its robustness in adapting to various fault conditions. The Naive Bayes, Multilayer Perceptron, and Logistic Regression classifiers achieved lower accuracies of 78.6982%, 76.9231%, and 80.4734% respectively. These results indicate a significant improvement in fault detection capability, enhancing the stability, reliability, and resilience of electrical grids integrated with PV systems. The findings suggest that the Random Forest classifier is highly effective for HIF detection, which is crucial for the protection and efficient operation of modern power grids with high renewable energy penetration. |
| first_indexed | 2025-11-15T14:37:24Z |
| format | Article |
| id | upm-118346 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:37:24Z |
| publishDate | 2025 |
| publisher | IEEE Canada |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1183462025-07-09T04:38:21Z http://psasir.upm.edu.my/id/eprint/118346/ Performance analysis of intelligent classifiers for high impedance fault detection in a PV-integrated IEEE-13 bus system Mahzan, Najwa Nasuha Lutfi Othman, Mohammad Izzri Abdul Wahab, Noor Veerasamy, Veerapandiyan Salim, Nur Ashida Azwin Zainul Abidin, Aidil Zahurul Islam, Syed High impedance faults (HIFs) present significant challenges in power systems, particularly when an electrical wire contacts a high-resistance material, leading to low currents that are difficult for traditional relays to detect. With the increasing integration of photovoltaic (PV) systems, these challenges are exacerbated due to the complex behavior of PV-generated signals. This study aims to enhance the detection of HIFs in PV-integrated systems using advanced machine learning techniques. The approach employs various classifiers including artificial neural networks, support vector machines, decision trees, and Random Forest to improve fault identification accuracy. A MATLAB/SIMULINK simulation was conducted on an IEEE 13-bus system with a 300 kW solar PV plant. The Discrete Wavelet Transform (DWT) with the db4 wavelet was used for feature extraction, focusing on phase energy values. The classifiers were evaluated under different scenarios such as normal operation, load switching, capacitor switching, HIF, and line-to-ground (LG) faults. The Random Forest classifier outperformed others, achieving a fault detection accuracy of 99.4083%, demonstrating its robustness in adapting to various fault conditions. The Naive Bayes, Multilayer Perceptron, and Logistic Regression classifiers achieved lower accuracies of 78.6982%, 76.9231%, and 80.4734% respectively. These results indicate a significant improvement in fault detection capability, enhancing the stability, reliability, and resilience of electrical grids integrated with PV systems. The findings suggest that the Random Forest classifier is highly effective for HIF detection, which is crucial for the protection and efficient operation of modern power grids with high renewable energy penetration. IEEE Canada 2025 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/118346/1/118346.pdf Mahzan, Najwa Nasuha and Lutfi Othman, Mohammad and Izzri Abdul Wahab, Noor and Veerasamy, Veerapandiyan and Salim, Nur Ashida and Azwin Zainul Abidin, Aidil and Zahurul Islam, Syed (2025) Performance analysis of intelligent classifiers for high impedance fault detection in a PV-integrated IEEE-13 bus system. IEEE Canadian Journal of Electrical and Computer Engineering, 48 (2). art. no. https://ieeexplore.ieee.org/document/10970376. pp. 98-108. ISSN 2694-1783 https://ieeexplore.ieee.org/document/10970376/ 10.1109/ICJECE.2024.3469216 |
| spellingShingle | Mahzan, Najwa Nasuha Lutfi Othman, Mohammad Izzri Abdul Wahab, Noor Veerasamy, Veerapandiyan Salim, Nur Ashida Azwin Zainul Abidin, Aidil Zahurul Islam, Syed Performance analysis of intelligent classifiers for high impedance fault detection in a PV-integrated IEEE-13 bus system |
| title | Performance analysis of intelligent classifiers for high impedance fault detection in a PV-integrated IEEE-13 bus system |
| title_full | Performance analysis of intelligent classifiers for high impedance fault detection in a PV-integrated IEEE-13 bus system |
| title_fullStr | Performance analysis of intelligent classifiers for high impedance fault detection in a PV-integrated IEEE-13 bus system |
| title_full_unstemmed | Performance analysis of intelligent classifiers for high impedance fault detection in a PV-integrated IEEE-13 bus system |
| title_short | Performance analysis of intelligent classifiers for high impedance fault detection in a PV-integrated IEEE-13 bus system |
| title_sort | performance analysis of intelligent classifiers for high impedance fault detection in a pv-integrated ieee-13 bus system |
| url | http://psasir.upm.edu.my/id/eprint/118346/ http://psasir.upm.edu.my/id/eprint/118346/ http://psasir.upm.edu.my/id/eprint/118346/ http://psasir.upm.edu.my/id/eprint/118346/1/118346.pdf |