Parameter estimation of submarine power cables in offshore applications using machine learning-based methods
Monitoring electrical parameters of power transmission systems is essential to ensure reliability and optimal operating conditions. This research presents an accurate methodology for estimation of the sequence parameters of submarine power cables using a data-driven approach based synchrophasor meas...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525004880 |
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| author | Felipe P. de Albuquerque Rafael Nascimento Gabriel de Castro Biage Rooney R.A. Coelho Ronaldo F. Ribeiro Pereira Eduardo C. Marques da Costa Mario L. Pereira Filho Cassio G. Lopes José R. Cardoso |
| author_facet | Felipe P. de Albuquerque Rafael Nascimento Gabriel de Castro Biage Rooney R.A. Coelho Ronaldo F. Ribeiro Pereira Eduardo C. Marques da Costa Mario L. Pereira Filho Cassio G. Lopes José R. Cardoso |
| author_sort | Felipe P. de Albuquerque |
| collection | DOAJ |
| description | Monitoring electrical parameters of power transmission systems is essential to ensure reliability and optimal operating conditions. This research presents an accurate methodology for estimation of the sequence parameters of submarine power cables using a data-driven approach based synchrophasor measurements. Contrarily of conventional techniques, the proposed methodology is based on supervised machine learning models trained on realistic simulations, which incorporate the physical and geometric characteristics of the power cable, with its seven propagation modes. In practical conditions, the training dataset takes into account noise patterns using well-established modeling methods for phasor measurements. These patterns include time-correlated and statistically coherent disturbances, which are representative of those typically encountered in systems employing Phasor Measurement Units (PMUs). A detailed statistical investigation was also conducted to characterize the empirical distribution of the input data, supporting model design and validation. Remarkably, the proposed algorithm achieves accurate parameter estimation even under elevated noise conditions, requiring as few as 200 training samples. The maximum observed estimation error was approximately 1%, underscoring the robustness, efficiency, and practical viability of the proposed framework for the electrical characterization of submarine transmission systems. |
| format | Article |
| id | doaj-art-d8c3568f51bc4ec79df9576747440b14 |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-d8c3568f51bc4ec79df9576747440b142025-08-20T03:44:07ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-10-0117111094010.1016/j.ijepes.2025.110940Parameter estimation of submarine power cables in offshore applications using machine learning-based methodsFelipe P. de Albuquerque0Rafael Nascimento1Gabriel de Castro Biage2Rooney R.A. Coelho3Ronaldo F. Ribeiro Pereira4Eduardo C. Marques da Costa5Mario L. Pereira Filho6Cassio G. Lopes7José R. Cardoso8UFMT - Universidade Federal do Mato Grosso, Cuiabá, BrazilUSP - University of São Paulo, Polytechnic School, São Paulo, BrazilUSP - University of São Paulo, Polytechnic School, São Paulo, BrazilUSP - University of São Paulo, Polytechnic School, São Paulo, BrazilUFAC - Federal University of Acre, Rio Branco, BrazilUSP - University of São Paulo, Polytechnic School, São Paulo, Brazil; Corresponding author.USP - University of São Paulo, Polytechnic School, São Paulo, BrazilUSP - University of São Paulo, Polytechnic School, São Paulo, BrazilUSP - University of São Paulo, Polytechnic School, São Paulo, BrazilMonitoring electrical parameters of power transmission systems is essential to ensure reliability and optimal operating conditions. This research presents an accurate methodology for estimation of the sequence parameters of submarine power cables using a data-driven approach based synchrophasor measurements. Contrarily of conventional techniques, the proposed methodology is based on supervised machine learning models trained on realistic simulations, which incorporate the physical and geometric characteristics of the power cable, with its seven propagation modes. In practical conditions, the training dataset takes into account noise patterns using well-established modeling methods for phasor measurements. These patterns include time-correlated and statistically coherent disturbances, which are representative of those typically encountered in systems employing Phasor Measurement Units (PMUs). A detailed statistical investigation was also conducted to characterize the empirical distribution of the input data, supporting model design and validation. Remarkably, the proposed algorithm achieves accurate parameter estimation even under elevated noise conditions, requiring as few as 200 training samples. The maximum observed estimation error was approximately 1%, underscoring the robustness, efficiency, and practical viability of the proposed framework for the electrical characterization of submarine transmission systems.http://www.sciencedirect.com/science/article/pii/S0142061525004880Submarine cablesParameter estimationMachine learningPhasor measurementsPower transmission |
| spellingShingle | Felipe P. de Albuquerque Rafael Nascimento Gabriel de Castro Biage Rooney R.A. Coelho Ronaldo F. Ribeiro Pereira Eduardo C. Marques da Costa Mario L. Pereira Filho Cassio G. Lopes José R. Cardoso Parameter estimation of submarine power cables in offshore applications using machine learning-based methods International Journal of Electrical Power & Energy Systems Submarine cables Parameter estimation Machine learning Phasor measurements Power transmission |
| title | Parameter estimation of submarine power cables in offshore applications using machine learning-based methods |
| title_full | Parameter estimation of submarine power cables in offshore applications using machine learning-based methods |
| title_fullStr | Parameter estimation of submarine power cables in offshore applications using machine learning-based methods |
| title_full_unstemmed | Parameter estimation of submarine power cables in offshore applications using machine learning-based methods |
| title_short | Parameter estimation of submarine power cables in offshore applications using machine learning-based methods |
| title_sort | parameter estimation of submarine power cables in offshore applications using machine learning based methods |
| topic | Submarine cables Parameter estimation Machine learning Phasor measurements Power transmission |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525004880 |
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