Artificial neural network application in an implemented lightning locating system
Time difference of arrival (TDOA) technique is one of many bases to determine lightning strike location employed in a lightning locating system (LLS). In this technique, at least four measurement sensors are required to correctly locate a lightning strike. Usage of fewer number of sensors will resul...
| Main Authors: | , , , , , |
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| Format: | Article |
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Elsevier Ltd
2020
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| _version_ | 1796865294787936256 |
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| author | Mehranzamir, Kamyar Abdul Malek, Zulkurnain Afrouzi, Hadi Nabipour Mashak, Saeed Vahabi Wooi, Chin Leong Zarei, Roozbeh |
| author_facet | Mehranzamir, Kamyar Abdul Malek, Zulkurnain Afrouzi, Hadi Nabipour Mashak, Saeed Vahabi Wooi, Chin Leong Zarei, Roozbeh |
| author_sort | Mehranzamir, Kamyar |
| collection | ePrints |
| description | Time difference of arrival (TDOA) technique is one of many bases to determine lightning strike location employed in a lightning locating system (LLS). In this technique, at least four measurement sensors are required to correctly locate a lightning strike. Usage of fewer number of sensors will result in non-unique solutions to the generated hyperbolas, and hence wrong lightning strike point. This research aims to correctly determine the strike point even if only three measuring sensors are utilized. An artificial neural network (ANN) based algorithm was developed for a 400 km2 coverage area in Southern Malaysia using time of arrival data collected at the three measuring stations over a certain period. The Levenberg–Marquardt algorithm is demonstrated to correctly identify the lightning strike coordinates with an average error of 350 m. The algorithm has helped the three-station TDOA-based LLS to successfully locate the lightning strike point with a remarkable accuracy comparable to that of commercial systems. |
| first_indexed | 2024-03-05T20:54:48Z |
| format | Article |
| id | utm.eprints-91778 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:54:48Z |
| publishDate | 2020 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | utm.eprints-917782021-07-28T08:42:47Z http://eprints.utm.my/91778/ Artificial neural network application in an implemented lightning locating system Mehranzamir, Kamyar Abdul Malek, Zulkurnain Afrouzi, Hadi Nabipour Mashak, Saeed Vahabi Wooi, Chin Leong Zarei, Roozbeh TK Electrical engineering. Electronics Nuclear engineering Time difference of arrival (TDOA) technique is one of many bases to determine lightning strike location employed in a lightning locating system (LLS). In this technique, at least four measurement sensors are required to correctly locate a lightning strike. Usage of fewer number of sensors will result in non-unique solutions to the generated hyperbolas, and hence wrong lightning strike point. This research aims to correctly determine the strike point even if only three measuring sensors are utilized. An artificial neural network (ANN) based algorithm was developed for a 400 km2 coverage area in Southern Malaysia using time of arrival data collected at the three measuring stations over a certain period. The Levenberg–Marquardt algorithm is demonstrated to correctly identify the lightning strike coordinates with an average error of 350 m. The algorithm has helped the three-station TDOA-based LLS to successfully locate the lightning strike point with a remarkable accuracy comparable to that of commercial systems. Elsevier Ltd 2020-11-15 Article PeerReviewed Mehranzamir, Kamyar and Abdul Malek, Zulkurnain and Afrouzi, Hadi Nabipour and Mashak, Saeed Vahabi and Wooi, Chin Leong and Zarei, Roozbeh (2020) Artificial neural network application in an implemented lightning locating system. Journal of Atmospheric and Solar-Terrestrial Physics, 210 . ISSN 1364-6826 http://dx.doi.org/10.1016/j.jastp.2020.105437 DOI:10.1016/j.jastp.2020.105437 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mehranzamir, Kamyar Abdul Malek, Zulkurnain Afrouzi, Hadi Nabipour Mashak, Saeed Vahabi Wooi, Chin Leong Zarei, Roozbeh Artificial neural network application in an implemented lightning locating system |
| title | Artificial neural network application in an implemented lightning locating system |
| title_full | Artificial neural network application in an implemented lightning locating system |
| title_fullStr | Artificial neural network application in an implemented lightning locating system |
| title_full_unstemmed | Artificial neural network application in an implemented lightning locating system |
| title_short | Artificial neural network application in an implemented lightning locating system |
| title_sort | artificial neural network application in an implemented lightning locating system |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT mehranzamirkamyar artificialneuralnetworkapplicationinanimplementedlightninglocatingsystem AT abdulmalekzulkurnain artificialneuralnetworkapplicationinanimplementedlightninglocatingsystem AT afrouzihadinabipour artificialneuralnetworkapplicationinanimplementedlightninglocatingsystem AT mashaksaeedvahabi artificialneuralnetworkapplicationinanimplementedlightninglocatingsystem AT wooichinleong artificialneuralnetworkapplicationinanimplementedlightninglocatingsystem AT zareiroozbeh artificialneuralnetworkapplicationinanimplementedlightninglocatingsystem |