A novel microgrid fault detection and classification method using maximal overlap discrete wavelet packet transform and an augmented Lagrangian particle swarm optimization-support vector machine
In this paper, an intelligent method for fault detection and classification for a microgrid (MG) was proposed. The idea was based on the combination of three computational tools: signal processing using the maximal overlap discrete wavelet packet transform (MODWPT), parameter optimization by the aug...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2022
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| Online Access: | http://eprints.utm.my/103644/1/ZainalSalam2022_ANovelMicrogridFaultDetectionandClassification.pdf |
| _version_ | 1796867500900614144 |
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| author | Ahmadipour, Masoud Othman, Muhammad Murtadha Bo, Rui Salam, Zainal Mohammed Ridha, Hussein Hasan, Kamrul |
| author_facet | Ahmadipour, Masoud Othman, Muhammad Murtadha Bo, Rui Salam, Zainal Mohammed Ridha, Hussein Hasan, Kamrul |
| author_sort | Ahmadipour, Masoud |
| collection | ePrints |
| description | In this paper, an intelligent method for fault detection and classification for a microgrid (MG) was proposed. The idea was based on the combination of three computational tools: signal processing using the maximal overlap discrete wavelet packet transform (MODWPT), parameter optimization by the augmented Lagrangian particle swarm optimization (ALPSO), and machine learning using the support vector machine (SVM). The MODWPT was applied to preprocess half cycle of the post-fault current samples measured at both ends of feeders. The wavelet coefficients derived from the MODWPT were statistically evaluated using the mean, standard deviation, energy, skewness, kurtosis, logarithmic energy entropy, max, min, and Shannon entropy. These were the input feature datasets and were used to train the SVM classifier. The ALPSO was utilized to reduce the feature subsets and select the sensitive parameters of the SVM (i.e., penalty factor and the slack variable) to further improve the performance of the SVM. The intelligent relaying scheme was executed on a real-time digital simulator (RTDS) which is integrated with Matlab. The performance of SVM-based protection method is compared to several different protection models in terms of signal processing tools, optimization techniques used for selecting datasets and sensitive parameters, and classifiers under different operating conditions. Numerous operating conditions, including islanded or non-islanded operation modes and radial and or loop topologies introducing different characteristics of fault were included as the case studies for the proposed technique. A comprehensive evaluation study of the consortium for electric reliability technology solutions (CERTS) MG system and IEEE 34-bus confirms that the proposed protection scheme is accurate, fast, and robust to noisy measurements. In addition, the obtained results illustrate that the proposed method is superior to the recently published works in the literature. |
| first_indexed | 2024-03-05T21:28:12Z |
| format | Article |
| id | utm.eprints-103644 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:28:12Z |
| publishDate | 2022 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | utm.eprints-1036442023-11-23T08:11:14Z http://eprints.utm.my/103644/ A novel microgrid fault detection and classification method using maximal overlap discrete wavelet packet transform and an augmented Lagrangian particle swarm optimization-support vector machine Ahmadipour, Masoud Othman, Muhammad Murtadha Bo, Rui Salam, Zainal Mohammed Ridha, Hussein Hasan, Kamrul TK Electrical engineering. Electronics Nuclear engineering In this paper, an intelligent method for fault detection and classification for a microgrid (MG) was proposed. The idea was based on the combination of three computational tools: signal processing using the maximal overlap discrete wavelet packet transform (MODWPT), parameter optimization by the augmented Lagrangian particle swarm optimization (ALPSO), and machine learning using the support vector machine (SVM). The MODWPT was applied to preprocess half cycle of the post-fault current samples measured at both ends of feeders. The wavelet coefficients derived from the MODWPT were statistically evaluated using the mean, standard deviation, energy, skewness, kurtosis, logarithmic energy entropy, max, min, and Shannon entropy. These were the input feature datasets and were used to train the SVM classifier. The ALPSO was utilized to reduce the feature subsets and select the sensitive parameters of the SVM (i.e., penalty factor and the slack variable) to further improve the performance of the SVM. The intelligent relaying scheme was executed on a real-time digital simulator (RTDS) which is integrated with Matlab. The performance of SVM-based protection method is compared to several different protection models in terms of signal processing tools, optimization techniques used for selecting datasets and sensitive parameters, and classifiers under different operating conditions. Numerous operating conditions, including islanded or non-islanded operation modes and radial and or loop topologies introducing different characteristics of fault were included as the case studies for the proposed technique. A comprehensive evaluation study of the consortium for electric reliability technology solutions (CERTS) MG system and IEEE 34-bus confirms that the proposed protection scheme is accurate, fast, and robust to noisy measurements. In addition, the obtained results illustrate that the proposed method is superior to the recently published works in the literature. Elsevier Ltd 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/103644/1/ZainalSalam2022_ANovelMicrogridFaultDetectionandClassification.pdf Ahmadipour, Masoud and Othman, Muhammad Murtadha and Bo, Rui and Salam, Zainal and Mohammed Ridha, Hussein and Hasan, Kamrul (2022) A novel microgrid fault detection and classification method using maximal overlap discrete wavelet packet transform and an augmented Lagrangian particle swarm optimization-support vector machine. Energy Reports, 8 (NA). pp. 4854-4870. ISSN 2352-4847 http://dx.doi.org/10.1016/j.egyr.2022.03.174 DOI : 10.1016/j.egyr.2022.03.174 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Ahmadipour, Masoud Othman, Muhammad Murtadha Bo, Rui Salam, Zainal Mohammed Ridha, Hussein Hasan, Kamrul A novel microgrid fault detection and classification method using maximal overlap discrete wavelet packet transform and an augmented Lagrangian particle swarm optimization-support vector machine |
| title | A novel microgrid fault detection and classification method using maximal overlap discrete wavelet packet transform and an augmented Lagrangian particle swarm optimization-support vector machine |
| title_full | A novel microgrid fault detection and classification method using maximal overlap discrete wavelet packet transform and an augmented Lagrangian particle swarm optimization-support vector machine |
| title_fullStr | A novel microgrid fault detection and classification method using maximal overlap discrete wavelet packet transform and an augmented Lagrangian particle swarm optimization-support vector machine |
| title_full_unstemmed | A novel microgrid fault detection and classification method using maximal overlap discrete wavelet packet transform and an augmented Lagrangian particle swarm optimization-support vector machine |
| title_short | A novel microgrid fault detection and classification method using maximal overlap discrete wavelet packet transform and an augmented Lagrangian particle swarm optimization-support vector machine |
| title_sort | novel microgrid fault detection and classification method using maximal overlap discrete wavelet packet transform and an augmented lagrangian particle swarm optimization support vector machine |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/103644/1/ZainalSalam2022_ANovelMicrogridFaultDetectionandClassification.pdf |
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