LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system
This paper presents the detection of High Impedance Fault (HIF) in solar Photovoltaic (PV) integrated power system using recurrent neural network-based Long Short-Term Memory (LSTM) approach. For study this, an IEEE 13-bus system was modeled in MATLAB/Simulink environment to integrate 300 kW solar P...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/94060/ |
| _version_ | 1848861906509496320 |
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| author | Veerasamy, Veerapandiyan Abdul Wahab, Noor Izzri Othman, Mohammad Lutfi Padmanaban, Sanjeevikumar Sekar, Kavaskar Ramachandran, Rajeswari Hizam, Hashim Vinayagam, Arangarajan Islam, Mohammad Zohrul |
| author_facet | Veerasamy, Veerapandiyan Abdul Wahab, Noor Izzri Othman, Mohammad Lutfi Padmanaban, Sanjeevikumar Sekar, Kavaskar Ramachandran, Rajeswari Hizam, Hashim Vinayagam, Arangarajan Islam, Mohammad Zohrul |
| author_sort | Veerasamy, Veerapandiyan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper presents the detection of High Impedance Fault (HIF) in solar Photovoltaic (PV) integrated power system using recurrent neural network-based Long Short-Term Memory (LSTM) approach. For study this, an IEEE 13-bus system was modeled in MATLAB/Simulink environment to integrate 300 kW solar PV systems for analysis. Initially, the three-phase current signal during non-faulty (regular operation, capacitor switching, load switching, transformer inrush current) and faulty (HIF, symmetrical and unsymmetrical fault) conditions were used for extraction of features. The signal processing technique of Discrete Wavelet Transform with db4 mother wavelet was applied to extract each phase's energy value features for training and testing the classifiers. The proposed LSTM classifier gives the overall classification accuracy of 91.21% with a success rate of 92.42 % in identifying HIF in PV integrated power network. The prediction results obtained from the proffered method are compared with other well-known classifiers of K-Nearest neighbor's network, Support vector machine, J48 based decision tree, and Naïve Bayes approach. Further, the classifier's robustness is validated by evaluating the performance indices (PI) of kappa statistic, precision, recall, and F-measure. The results obtained reveal that the proposed LSTM network significantly outperforms all PI compared to other techniques. |
| first_indexed | 2025-11-15T13:08:34Z |
| format | Article |
| id | upm-94060 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:08:34Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-940602023-04-06T02:19:02Z http://psasir.upm.edu.my/id/eprint/94060/ LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system Veerasamy, Veerapandiyan Abdul Wahab, Noor Izzri Othman, Mohammad Lutfi Padmanaban, Sanjeevikumar Sekar, Kavaskar Ramachandran, Rajeswari Hizam, Hashim Vinayagam, Arangarajan Islam, Mohammad Zohrul This paper presents the detection of High Impedance Fault (HIF) in solar Photovoltaic (PV) integrated power system using recurrent neural network-based Long Short-Term Memory (LSTM) approach. For study this, an IEEE 13-bus system was modeled in MATLAB/Simulink environment to integrate 300 kW solar PV systems for analysis. Initially, the three-phase current signal during non-faulty (regular operation, capacitor switching, load switching, transformer inrush current) and faulty (HIF, symmetrical and unsymmetrical fault) conditions were used for extraction of features. The signal processing technique of Discrete Wavelet Transform with db4 mother wavelet was applied to extract each phase's energy value features for training and testing the classifiers. The proposed LSTM classifier gives the overall classification accuracy of 91.21% with a success rate of 92.42 % in identifying HIF in PV integrated power network. The prediction results obtained from the proffered method are compared with other well-known classifiers of K-Nearest neighbor's network, Support vector machine, J48 based decision tree, and Naïve Bayes approach. Further, the classifier's robustness is validated by evaluating the performance indices (PI) of kappa statistic, precision, recall, and F-measure. The results obtained reveal that the proposed LSTM network significantly outperforms all PI compared to other techniques. Institute of Electrical and Electronics Engineers 2021 Article PeerReviewed Veerasamy, Veerapandiyan and Abdul Wahab, Noor Izzri and Othman, Mohammad Lutfi and Padmanaban, Sanjeevikumar and Sekar, Kavaskar and Ramachandran, Rajeswari and Hizam, Hashim and Vinayagam, Arangarajan and Islam, Mohammad Zohrul (2021) LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system. IEEE Access, 9 (2021). 32672 - 32687. ISSN 2169-3536 https://ieeexplore.ieee.org/document/9359758 10.1109/ACCESS.2021.3060800 |
| spellingShingle | Veerasamy, Veerapandiyan Abdul Wahab, Noor Izzri Othman, Mohammad Lutfi Padmanaban, Sanjeevikumar Sekar, Kavaskar Ramachandran, Rajeswari Hizam, Hashim Vinayagam, Arangarajan Islam, Mohammad Zohrul LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system |
| title | LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system |
| title_full | LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system |
| title_fullStr | LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system |
| title_full_unstemmed | LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system |
| title_short | LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system |
| title_sort | lstm recurrent neural network classifier for high impedance fault detection in solar pv integrated power system |
| url | http://psasir.upm.edu.my/id/eprint/94060/ http://psasir.upm.edu.my/id/eprint/94060/ http://psasir.upm.edu.my/id/eprint/94060/ |