Bayesian Prony Modal Identification and Hierarchical Control Strategy for Low-Frequency Oscillation of Ship Microgrid
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| Publicado en: | Electronics vol. 14, no. 23 (2025), p. 4669-4691 |
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| Otros Autores: | , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14234669 |2 doi | |
| 035 | |a 3280947587 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Ding, Yue | |
| 245 | 1 | |a Bayesian Prony Modal Identification and Hierarchical Control Strategy for Low-Frequency Oscillation of Ship Microgrid | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a A Bayes–Prony oscillating modal identification and hierarchical control strategy for low-frequency oscillation (LFO) of a ship microgrid (SM) is presented in this paper. The modal probabilistic estimation of the proposed algorithm replaces point estimation of the traditional Prony method and improves the robustness of modal identification. The hierarchical control strategy first performs modal identification by means of the batch least squares Prony (BLS-Prony) algorithm. The modal identification results are calibrated by the explanatory variance score (EVS), and the control process is transferred to recursive least squares Prony (RLS-Prony) real-time detection. The third layer of decision making transfers to Bayesian Prony (Bayes–Prony) identification in case of a loss of modality or failure of identification. The designed Bayes–Prony algorithm identifies the oscillatory modal of signals with a signal-to-noise ratio (SNR) equal to 2 dB. Compared to BLS-Prony and RLS-Prony, Bayes–Prony reduces the SNR convergence domain of the signal by 30 dB as the last layer of hierarchical control. Therefore, the third-layer decision commands are used as a scheduling reference for damping control in SM power plants. The proposed algorithms and strategies maximize the saving of computational resources while ensuring that the modal identification is effective. Finally, the correctness of the proposed algorithm and strategy is verified by the LFO waveforms of the experimental platform. | |
| 610 | 4 | |a International Maritime Organization | |
| 653 | |a Modal identification | ||
| 653 | |a Waveforms | ||
| 653 | |a Deep learning | ||
| 653 | |a Distributed generation | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Prony's method | ||
| 653 | |a Recursive functions | ||
| 653 | |a Damping | ||
| 653 | |a Algorithms | ||
| 653 | |a Energy storage | ||
| 653 | |a Time series | ||
| 653 | |a Real time | ||
| 653 | |a Power plants | ||
| 653 | |a Least squares | ||
| 653 | |a Shipping industry | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Signal to noise ratio | ||
| 700 | 1 | |a Zhao, Ke | |
| 700 | 1 | |a Duan Jiandong | |
| 700 | 1 | |a Sun, Li | |
| 773 | 0 | |t Electronics |g vol. 14, no. 23 (2025), p. 4669-4691 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3280947587/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3280947587/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3280947587/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |