Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network

Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can d...
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Autor*in:	Aleksandar Cuculić [verfasserIn] Luka Draščić [verfasserIn] Ivan Panić [verfasserIn] Jasmin Ćelić [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	hybrid-electric ferry maritime transport marine electrical systems electrical power disturbances wavelet transform neural network

Übergeordnetes Werk:	In: Journal of Marine Science and Engineering - MDPI AG, 2014, 10(2022), 9, p 1190
Übergeordnetes Werk:	volume:10 ; year:2022 ; number:9, p 1190

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/jmse10091190

Katalog-ID:	DOAJ084798467

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520			\|a Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can directly affect the safety and economic performance of the vessel. In this paper, a power disturbance classification method for hybrid-electric ferries is developed based on a wavelet transform and a neural network classifier. For each of the observed power disturbance categories, 200 signals were artificially generated. A discrete wavelet transform was applied to these signals, allowing different time-frequency resolutions to be used for different frequencies. Three statistical parameters are calculated for each coefficient: Standard deviation, entropy and asymmetry of the signal, providing a total of 18 variables for a signal. A neural network with 18 input neurons, 3 hidden neurons, and 6 output neurons was used to detect the aforementioned perturbations. The classification models with different wavelets were analyzed based on accuracy, confusion matrices, and other parameters. The analysis showed that the proposed model can be successfully used for the detection and classification of disturbances in the considered vessels, which allows the implementation of better and more efficient algorithms for energy management.
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allfields	10.3390/jmse10091190 doi (DE-627)DOAJ084798467 (DE-599)DOAJ769960c1f0134ef9b3639894d52eb6e7 DE-627 ger DE-627 rakwb eng VM1-989 GC1-1581 Aleksandar Cuculić verfasserin aut Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can directly affect the safety and economic performance of the vessel. In this paper, a power disturbance classification method for hybrid-electric ferries is developed based on a wavelet transform and a neural network classifier. For each of the observed power disturbance categories, 200 signals were artificially generated. A discrete wavelet transform was applied to these signals, allowing different time-frequency resolutions to be used for different frequencies. Three statistical parameters are calculated for each coefficient: Standard deviation, entropy and asymmetry of the signal, providing a total of 18 variables for a signal. A neural network with 18 input neurons, 3 hidden neurons, and 6 output neurons was used to detect the aforementioned perturbations. The classification models with different wavelets were analyzed based on accuracy, confusion matrices, and other parameters. The analysis showed that the proposed model can be successfully used for the detection and classification of disturbances in the considered vessels, which allows the implementation of better and more efficient algorithms for energy management. hybrid-electric ferry maritime transport marine electrical systems electrical power disturbances wavelet transform neural network Naval architecture. Shipbuilding. Marine engineering Oceanography Luka Draščić verfasserin aut Ivan Panić verfasserin aut Jasmin Ćelić verfasserin aut In Journal of Marine Science and Engineering MDPI AG, 2014 10(2022), 9, p 1190 (DE-627)771274181 (DE-600)2738390-8 20771312 nnns volume:10 year:2022 number:9, p 1190 https://doi.org/10.3390/jmse10091190 kostenfrei https://doaj.org/article/769960c1f0134ef9b3639894d52eb6e7 kostenfrei https://www.mdpi.com/2077-1312/10/9/1190 kostenfrei https://doaj.org/toc/2077-1312 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 9, p 1190
spelling	10.3390/jmse10091190 doi (DE-627)DOAJ084798467 (DE-599)DOAJ769960c1f0134ef9b3639894d52eb6e7 DE-627 ger DE-627 rakwb eng VM1-989 GC1-1581 Aleksandar Cuculić verfasserin aut Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can directly affect the safety and economic performance of the vessel. In this paper, a power disturbance classification method for hybrid-electric ferries is developed based on a wavelet transform and a neural network classifier. For each of the observed power disturbance categories, 200 signals were artificially generated. A discrete wavelet transform was applied to these signals, allowing different time-frequency resolutions to be used for different frequencies. Three statistical parameters are calculated for each coefficient: Standard deviation, entropy and asymmetry of the signal, providing a total of 18 variables for a signal. A neural network with 18 input neurons, 3 hidden neurons, and 6 output neurons was used to detect the aforementioned perturbations. The classification models with different wavelets were analyzed based on accuracy, confusion matrices, and other parameters. The analysis showed that the proposed model can be successfully used for the detection and classification of disturbances in the considered vessels, which allows the implementation of better and more efficient algorithms for energy management. hybrid-electric ferry maritime transport marine electrical systems electrical power disturbances wavelet transform neural network Naval architecture. Shipbuilding. Marine engineering Oceanography Luka Draščić verfasserin aut Ivan Panić verfasserin aut Jasmin Ćelić verfasserin aut In Journal of Marine Science and Engineering MDPI AG, 2014 10(2022), 9, p 1190 (DE-627)771274181 (DE-600)2738390-8 20771312 nnns volume:10 year:2022 number:9, p 1190 https://doi.org/10.3390/jmse10091190 kostenfrei https://doaj.org/article/769960c1f0134ef9b3639894d52eb6e7 kostenfrei https://www.mdpi.com/2077-1312/10/9/1190 kostenfrei https://doaj.org/toc/2077-1312 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 9, p 1190
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allfieldsGer	10.3390/jmse10091190 doi (DE-627)DOAJ084798467 (DE-599)DOAJ769960c1f0134ef9b3639894d52eb6e7 DE-627 ger DE-627 rakwb eng VM1-989 GC1-1581 Aleksandar Cuculić verfasserin aut Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can directly affect the safety and economic performance of the vessel. In this paper, a power disturbance classification method for hybrid-electric ferries is developed based on a wavelet transform and a neural network classifier. For each of the observed power disturbance categories, 200 signals were artificially generated. A discrete wavelet transform was applied to these signals, allowing different time-frequency resolutions to be used for different frequencies. Three statistical parameters are calculated for each coefficient: Standard deviation, entropy and asymmetry of the signal, providing a total of 18 variables for a signal. A neural network with 18 input neurons, 3 hidden neurons, and 6 output neurons was used to detect the aforementioned perturbations. The classification models with different wavelets were analyzed based on accuracy, confusion matrices, and other parameters. The analysis showed that the proposed model can be successfully used for the detection and classification of disturbances in the considered vessels, which allows the implementation of better and more efficient algorithms for energy management. hybrid-electric ferry maritime transport marine electrical systems electrical power disturbances wavelet transform neural network Naval architecture. Shipbuilding. Marine engineering Oceanography Luka Draščić verfasserin aut Ivan Panić verfasserin aut Jasmin Ćelić verfasserin aut In Journal of Marine Science and Engineering MDPI AG, 2014 10(2022), 9, p 1190 (DE-627)771274181 (DE-600)2738390-8 20771312 nnns volume:10 year:2022 number:9, p 1190 https://doi.org/10.3390/jmse10091190 kostenfrei https://doaj.org/article/769960c1f0134ef9b3639894d52eb6e7 kostenfrei https://www.mdpi.com/2077-1312/10/9/1190 kostenfrei https://doaj.org/toc/2077-1312 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 9, p 1190
allfieldsSound	10.3390/jmse10091190 doi (DE-627)DOAJ084798467 (DE-599)DOAJ769960c1f0134ef9b3639894d52eb6e7 DE-627 ger DE-627 rakwb eng VM1-989 GC1-1581 Aleksandar Cuculić verfasserin aut Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can directly affect the safety and economic performance of the vessel. In this paper, a power disturbance classification method for hybrid-electric ferries is developed based on a wavelet transform and a neural network classifier. For each of the observed power disturbance categories, 200 signals were artificially generated. A discrete wavelet transform was applied to these signals, allowing different time-frequency resolutions to be used for different frequencies. Three statistical parameters are calculated for each coefficient: Standard deviation, entropy and asymmetry of the signal, providing a total of 18 variables for a signal. A neural network with 18 input neurons, 3 hidden neurons, and 6 output neurons was used to detect the aforementioned perturbations. The classification models with different wavelets were analyzed based on accuracy, confusion matrices, and other parameters. The analysis showed that the proposed model can be successfully used for the detection and classification of disturbances in the considered vessels, which allows the implementation of better and more efficient algorithms for energy management. hybrid-electric ferry maritime transport marine electrical systems electrical power disturbances wavelet transform neural network Naval architecture. Shipbuilding. Marine engineering Oceanography Luka Draščić verfasserin aut Ivan Panić verfasserin aut Jasmin Ćelić verfasserin aut In Journal of Marine Science and Engineering MDPI AG, 2014 10(2022), 9, p 1190 (DE-627)771274181 (DE-600)2738390-8 20771312 nnns volume:10 year:2022 number:9, p 1190 https://doi.org/10.3390/jmse10091190 kostenfrei https://doaj.org/article/769960c1f0134ef9b3639894d52eb6e7 kostenfrei https://www.mdpi.com/2077-1312/10/9/1190 kostenfrei https://doaj.org/toc/2077-1312 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 9, p 1190
language	English
source	In Journal of Marine Science and Engineering 10(2022), 9, p 1190 volume:10 year:2022 number:9, p 1190
sourceStr	In Journal of Marine Science and Engineering 10(2022), 9, p 1190 volume:10 year:2022 number:9, p 1190
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	hybrid-electric ferry maritime transport marine electrical systems electrical power disturbances wavelet transform neural network Naval architecture. Shipbuilding. Marine engineering Oceanography
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container_title	Journal of Marine Science and Engineering
authorswithroles_txt_mv	Aleksandar Cuculić @@aut@@ Luka Draščić @@aut@@ Ivan Panić @@aut@@ Jasmin Ćelić @@aut@@
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callnumber-first	V - Naval Science
author	Aleksandar Cuculić
spellingShingle	Aleksandar Cuculić misc VM1-989 misc GC1-1581 misc hybrid-electric ferry misc maritime transport misc marine electrical systems misc electrical power disturbances misc wavelet transform misc neural network misc Naval architecture. Shipbuilding. Marine engineering misc Oceanography Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network
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topic_title	VM1-989 GC1-1581 Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network hybrid-electric ferry maritime transport marine electrical systems electrical power disturbances wavelet transform neural network
topic	misc VM1-989 misc GC1-1581 misc hybrid-electric ferry misc maritime transport misc marine electrical systems misc electrical power disturbances misc wavelet transform misc neural network misc Naval architecture. Shipbuilding. Marine engineering misc Oceanography
topic_unstemmed	misc VM1-989 misc GC1-1581 misc hybrid-electric ferry misc maritime transport misc marine electrical systems misc electrical power disturbances misc wavelet transform misc neural network misc Naval architecture. Shipbuilding. Marine engineering misc Oceanography
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title	Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network
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title_full	Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network
author_sort	Aleksandar Cuculić
journal	Journal of Marine Science and Engineering
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author_browse	Aleksandar Cuculić Luka Draščić Ivan Panić Jasmin Ćelić
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title_sort	classification of electrical power disturbances on hybrid-electric ferries using wavelet transform and neural network
callnumber	VM1-989
title_auth	Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network
abstract	Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can directly affect the safety and economic performance of the vessel. In this paper, a power disturbance classification method for hybrid-electric ferries is developed based on a wavelet transform and a neural network classifier. For each of the observed power disturbance categories, 200 signals were artificially generated. A discrete wavelet transform was applied to these signals, allowing different time-frequency resolutions to be used for different frequencies. Three statistical parameters are calculated for each coefficient: Standard deviation, entropy and asymmetry of the signal, providing a total of 18 variables for a signal. A neural network with 18 input neurons, 3 hidden neurons, and 6 output neurons was used to detect the aforementioned perturbations. The classification models with different wavelets were analyzed based on accuracy, confusion matrices, and other parameters. The analysis showed that the proposed model can be successfully used for the detection and classification of disturbances in the considered vessels, which allows the implementation of better and more efficient algorithms for energy management.
abstractGer	Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can directly affect the safety and economic performance of the vessel. In this paper, a power disturbance classification method for hybrid-electric ferries is developed based on a wavelet transform and a neural network classifier. For each of the observed power disturbance categories, 200 signals were artificially generated. A discrete wavelet transform was applied to these signals, allowing different time-frequency resolutions to be used for different frequencies. Three statistical parameters are calculated for each coefficient: Standard deviation, entropy and asymmetry of the signal, providing a total of 18 variables for a signal. A neural network with 18 input neurons, 3 hidden neurons, and 6 output neurons was used to detect the aforementioned perturbations. The classification models with different wavelets were analyzed based on accuracy, confusion matrices, and other parameters. The analysis showed that the proposed model can be successfully used for the detection and classification of disturbances in the considered vessels, which allows the implementation of better and more efficient algorithms for energy management.
abstract_unstemmed	Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can directly affect the safety and economic performance of the vessel. In this paper, a power disturbance classification method for hybrid-electric ferries is developed based on a wavelet transform and a neural network classifier. For each of the observed power disturbance categories, 200 signals were artificially generated. A discrete wavelet transform was applied to these signals, allowing different time-frequency resolutions to be used for different frequencies. Three statistical parameters are calculated for each coefficient: Standard deviation, entropy and asymmetry of the signal, providing a total of 18 variables for a signal. A neural network with 18 input neurons, 3 hidden neurons, and 6 output neurons was used to detect the aforementioned perturbations. The classification models with different wavelets were analyzed based on accuracy, confusion matrices, and other parameters. The analysis showed that the proposed model can be successfully used for the detection and classification of disturbances in the considered vessels, which allows the implementation of better and more efficient algorithms for energy management.
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title_short	Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network
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