| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Convolutional neural network based approach for static security assessment of power systems |
| Autor/-in: | Ramirez Gonzalez, Miguel Segundo Sevilla, Felix Rafael Korba, Petr |
| et. al: | No |
| Angaben zur Konferenz: | World Automation Congress 2021, virtual, 1-5 August 2021 |
| Erscheinungsdatum: | 4-Aug-2021 |
| Sprache: | Englisch |
| Schlagwörter: | Power system stability; Static security assessment; Convolutional neural network; Data-driven model; Deep learning |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnik |
| Zusammenfassung: | Steady-state response of the grid under a predefined set of credible contingencies is an important component of power system security assessment. With the growing complexity of electrical networks, fast and reliable methods and tools are required to effectively assist transmission grid operators in making decisions concerning system security procurement. In this regard, a Convolutional Neural Network (CNN) based approach to develop prediction models for static security assessment under N-1 contingency is investigated in this paper. The CNN model is trained and applied to classify the security status of a sample system according to given node voltage magnitudes, and active and reactive power injections at network buses. Considering a set of performance metrics, the superior performance of the CNN alternative is demonstrated by comparing the obtained results with a support vector machine classifier algorithm. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/22971 |
| Volltext Version: | Akzeptierte 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., Segundo Sevilla, F. R., & Korba, P. (2021, August 4). Convolutional neural network based approach for static security assessment of power systems. World Automation Congress 2021, Virtual, 1-5 August 2021.
Ramirez Gonzalez, M., Segundo Sevilla, F.R. and Korba, P. (2021) ‘Convolutional neural network based approach for static security assessment of power systems’, in World Automation Congress 2021, virtual, 1-5 August 2021.
M. Ramirez Gonzalez, F. R. Segundo Sevilla, and P. Korba, “Convolutional neural network based approach for static security assessment of power systems,” in World Automation Congress 2021, virtual, 1-5 August 2021, Aug. 2021.
RAMIREZ GONZALEZ, Miguel, Felix Rafael SEGUNDO SEVILLA und Petr KORBA, 2021. Convolutional neural network based approach for static security assessment of power systems. In: World Automation Congress 2021, virtual, 1-5 August 2021. Conference paper. 4 August 2021
Ramirez Gonzalez, Miguel, Felix Rafael Segundo Sevilla, and Petr Korba. 2021. “Convolutional Neural Network Based Approach for Static Security Assessment of Power Systems.” Conference paper. In World Automation Congress 2021, Virtual, 1-5 August 2021.
Ramirez Gonzalez, Miguel, et al. “Convolutional Neural Network Based Approach for Static Security Assessment of Power Systems.” World Automation Congress 2021, Virtual, 1-5 August 2021, 2021.
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