| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Small-signal stability assessment with transfer learning-based convolutional neural networks |
| Autor/-in: | Ramirez Gonzalez, Miguel Nösberger, Lukas Segundo Sevilla, Felix Rafael Korba, Petr |
| et. al: | No |
| DOI: | 10.1109/EPEC56903.2022.9999738 |
| Tagungsband: | 2022 IEEE Electrical Power and Energy Conference (EPEC) |
| Seite(n): | 386 |
| Seiten bis: | 391 |
| Angaben zur Konferenz: | 2022 IEEE Electrical Power and Energy Conference (EPEC), online, 5-7 December 2022 |
| Erscheinungsdatum: | 7-Dez-2022 |
| Verlag / Hrsg. Institution: | IEEE |
| ISBN: | 978-1-6654-6318-8 |
| ISSN: | 2381-2842 |
| Sprache: | Englisch |
| Schlagwörter: | Power system; Small-signal stability; Convolutional neural network; Transfer learning; Feature importance |
| Fachgebiet (DDC): | 621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnik |
| Zusammenfassung: | An approach for the small-signal stability assessment (SSSA) of power systems using a Convolutional Neural Network (CNN) model with transfer learning is presented in this paper. The concept of permutation feature importance (PFI) is included in model development to identify and drop the most irrelevant features in a given dataset, which minimizes the input information required by the model to achieve a certain performance and reduces the set of measurement locations for the related application. Then, a transfer learning approach using weight initialization and feature extraction is applied to leverage the knowledge of a pretrained model when a new independent dataset (obtained from the integration of converter-interfaced generation) is considered. Simulation results demonstrate that the transfer learning-based CNN model is able to exploit previous knowledge and provide a superior performance, as compared to the traditional rebuilt-from-scratch model. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/26641 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Energiesysteme und Fluid-Engineering (IEFE) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
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Ramirez Gonzalez, M., Nösberger, L., Segundo Sevilla, F. R., & Korba, P. (2022). Small-signal stability assessment with transfer learning-based convolutional neural networks [Conference paper]. 2022 IEEE Electrical Power and Energy Conference (EPEC), 386–391. https://doi.org/10.1109/EPEC56903.2022.9999738
Ramirez Gonzalez, M. et al. (2022) ‘Small-signal stability assessment with transfer learning-based convolutional neural networks’, in 2022 IEEE Electrical Power and Energy Conference (EPEC). IEEE, pp. 386–391. Available at: https://doi.org/10.1109/EPEC56903.2022.9999738.
M. Ramirez Gonzalez, L. Nösberger, F. R. Segundo Sevilla, and P. Korba, “Small-signal stability assessment with transfer learning-based convolutional neural networks,” in 2022 IEEE Electrical Power and Energy Conference (EPEC), Dec. 2022, pp. 386–391. doi: 10.1109/EPEC56903.2022.9999738.
RAMIREZ GONZALEZ, Miguel, Lukas NÖSBERGER, Felix Rafael SEGUNDO SEVILLA und Petr KORBA, 2022. Small-signal stability assessment with transfer learning-based convolutional neural networks. In: 2022 IEEE Electrical Power and Energy Conference (EPEC). Conference paper. IEEE. 7 Dezember 2022. S. 386–391. ISBN 978-1-6654-6318-8
Ramirez Gonzalez, Miguel, Lukas Nösberger, Felix Rafael Segundo Sevilla, and Petr Korba. 2022. “Small-Signal Stability Assessment with Transfer Learning-Based Convolutional Neural Networks.” Conference paper. In 2022 IEEE Electrical Power and Energy Conference (EPEC), 386–91. IEEE. https://doi.org/10.1109/EPEC56903.2022.9999738.
Ramirez Gonzalez, Miguel, et al. “Small-Signal Stability Assessment with Transfer Learning-Based Convolutional Neural Networks.” 2022 IEEE Electrical Power and Energy Conference (EPEC), IEEE, 2022, pp. 386–91, https://doi.org/10.1109/EPEC56903.2022.9999738.
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