Convolutional neural network based approach for static security assessment of power systems
| Autor: | Petr Korba, M. Ramirez-Gonzalez, F.R. Segundo Sevilla |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Deep learning Node (networking) Convolutional neural network 006: Spezielle Computerverfahren AC power Data-driven model Grid computer.software_genre Power system stability Set (abstract data type) 621.3: Elektro- Kommunikations- Steuerungs- und Regelungstechnik Electric power system Component (UML) Artificial intelligence Data mining business computer Static security assessment |
| Zdroj: | WAC |
| DOI: | 10.23919/wac50355.2021.9559458 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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