Comparative analysis of probabilistic neural network, radial basis function, and feed-forward neural network for fault classification in power distribution systems
This article presents a classification methodology based on probabilistic neural networks. To automatically select the training data and obtain the performance evaluation results, the “K-fold” cross-validation method is used. Then, the probabilistic neural network is compared with the feed-forward n...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/23169/1/Comparative%20analysis%20of%20probabilistic%20neural%20network%2C%20radial%20basis%20function%2C%20and%20feed-forward%20neural%20network%20for%20fault%20classification%20in%20power%20distribution%20systems.pdf |
| _version_ | 1796970306312601600 |
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| author | Mirzaei, Maryam Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Moazami, Ehsan |
| author_facet | Mirzaei, Maryam Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Moazami, Ehsan |
| author_sort | Mirzaei, Maryam |
| collection | UPM |
| description | This article presents a classification methodology based on probabilistic neural networks. To automatically select the training data and obtain the performance evaluation results, the “K-fold” cross-validation method is used. Then, the probabilistic neural network is compared with the feed-forward neural network and the radial basis function network. The goal is to propose a classifier that is capable of recognizing 11 classes of three-phase distribution system faults to solve the complex fault (three-phase short-circuit) classification problem for reducing the multiple-estimation problem to estimate the fault location in radial distribution systems. The data for the fault classifier is produced by DigSilent Power Factory, Integrated Power System Analysis Software on an IEEE 13-node test feeder. A selection of features or descriptors obtained from voltages and currents measured in the substation are analyzed and used as input of the probabilistic neural network classifier. It is shown that the probabilistic neural network approach can provide a fast and precise operation for various faults. The simulation results also show that the proposed model can successfully be used as an effective tool for solving complicated classification problems. |
| first_indexed | 2024-03-06T07:56:05Z |
| format | Article |
| id | upm.eprints-23169 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T07:56:05Z |
| publishDate | 2011 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | upm.eprints-231692015-12-09T05:07:12Z http://psasir.upm.edu.my/id/eprint/23169/ Comparative analysis of probabilistic neural network, radial basis function, and feed-forward neural network for fault classification in power distribution systems Mirzaei, Maryam Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Moazami, Ehsan This article presents a classification methodology based on probabilistic neural networks. To automatically select the training data and obtain the performance evaluation results, the “K-fold” cross-validation method is used. Then, the probabilistic neural network is compared with the feed-forward neural network and the radial basis function network. The goal is to propose a classifier that is capable of recognizing 11 classes of three-phase distribution system faults to solve the complex fault (three-phase short-circuit) classification problem for reducing the multiple-estimation problem to estimate the fault location in radial distribution systems. The data for the fault classifier is produced by DigSilent Power Factory, Integrated Power System Analysis Software on an IEEE 13-node test feeder. A selection of features or descriptors obtained from voltages and currents measured in the substation are analyzed and used as input of the probabilistic neural network classifier. It is shown that the probabilistic neural network approach can provide a fast and precise operation for various faults. The simulation results also show that the proposed model can successfully be used as an effective tool for solving complicated classification problems. Taylor & Francis 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/23169/1/Comparative%20analysis%20of%20probabilistic%20neural%20network%2C%20radial%20basis%20function%2C%20and%20feed-forward%20neural%20network%20for%20fault%20classification%20in%20power%20distribution%20systems.pdf Mirzaei, Maryam and Ab Kadir, Mohd Zainal Abidin and Hizam, Hashim and Moazami, Ehsan (2011) Comparative analysis of probabilistic neural network, radial basis function, and feed-forward neural network for fault classification in power distribution systems. Electric Power Components and Systems, 39 (16). pp. 1858-1871. ISSN 1532-5008; ESSN: 1532-5016 http://www.tandfonline.com/doi/abs/10.1080/15325008.2011.615802 10.1080/15325008.2011.615802 |
| spellingShingle | Mirzaei, Maryam Ab Kadir, Mohd Zainal Abidin Hizam, Hashim Moazami, Ehsan Comparative analysis of probabilistic neural network, radial basis function, and feed-forward neural network for fault classification in power distribution systems |
| title | Comparative analysis of probabilistic neural network, radial basis function, and feed-forward neural network for fault classification in power distribution systems |
| title_full | Comparative analysis of probabilistic neural network, radial basis function, and feed-forward neural network for fault classification in power distribution systems |
| title_fullStr | Comparative analysis of probabilistic neural network, radial basis function, and feed-forward neural network for fault classification in power distribution systems |
| title_full_unstemmed | Comparative analysis of probabilistic neural network, radial basis function, and feed-forward neural network for fault classification in power distribution systems |
| title_short | Comparative analysis of probabilistic neural network, radial basis function, and feed-forward neural network for fault classification in power distribution systems |
| title_sort | comparative analysis of probabilistic neural network radial basis function and feed forward neural network for fault classification in power distribution systems |
| url | http://psasir.upm.edu.my/id/eprint/23169/1/Comparative%20analysis%20of%20probabilistic%20neural%20network%2C%20radial%20basis%20function%2C%20and%20feed-forward%20neural%20network%20for%20fault%20classification%20in%20power%20distribution%20systems.pdf |
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