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Scientific paper - Original scientific paper
Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network
Journal of marine science and engineering, 10 (2022), 9; 1190. https://doi.org/10.3390/jmse10091190
Cuculić, Aleksandar; Draščić, Luka; Panić, Ivan; Ćelić, Jasmin

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Cuculić, A., Draščić, L., Panić, I. & Ćelić, J. (2022). Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network. Journal of marine science and engineering, 10. (9). doi: 10.3390/jmse10091190

Cuculić, Aleksandar, et al. "Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network." Journal of marine science and engineering, vol. 10, no. 9, 2022. https://doi.org/10.3390/jmse10091190

Cuculić, Aleksandar, Luka Draščić, Ivan Panić and Jasmin Ćelić. "Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network." Journal of marine science and engineering 10, no. 9 (2022). https://doi.org/10.3390/jmse10091190

Cuculić, A., et al. (2022) 'Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network', Journal of marine science and engineering, 10(9). doi: 10.3390/jmse10091190

Cuculić A, Draščić L, Panić I, Ćelić J. Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network. Journal of marine science and engineering [Internet]. 2022 [cited 2024 May 03];10(9). doi: 10.3390/jmse10091190

A. Cuculić, L. Draščić, I. Panić and J. Ćelić, "Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network", Journal of marine science and engineering, vol. 10, no. 9, 2022. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:187:044577. [Accessed: 03 May 2024]

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Metadata
Title (english)Classification of Electrical Power Disturbances on Hybrid-Electric Ferries Using Wavelet Transform and Neural Network
AuthorAleksandar Cuculić
MBZ: 325051
AuthorLuka Draščić
AuthorIvan Panić
MBZ: 371280
AuthorJasmin Ćelić
Author's institutionUniversity of Rijeka
Faculty of Maritime Studies, Rijeka
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Traffic and Transport Technology
Abstract (english)
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.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleJournal of marine science and engineering
Numberingvol. 10, no. 9, 1190
e-ISSN2077-1312
DOIhttps://doi.org/10.3390/jmse10091190
URN:NBNurn:nbn:hr:187:044577
Publication2022
Document URLhttp://bib.irb.hr/1210787
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2022-12-12 14:00:04