In large interconnected power systems, events such as the opening of transmission lines, connection/disconnection of large loads and high-power flows, may trigger electro-mechanical oscillations. If undamped, these oscillations may lead to blackouts. This Thesis employs a recently developed mathematical tool called Dynamic Mode Decomposition (DMD), to identify the predominant modes that characterize these oscillations. Based on this mathematical method, an algorithm is developed in Matlab and then tested with different types of data. Both advantages and disadvantages of the method used are reported. Lastly, a real-time monitoring tool for large power systems and a generalization of the method (OptDMD) are presented.
Nelle grosse reti elettriche di potenza, eventi come l'apertura di linee di trasmissione, la connessione/rimozione di grandi carichi ed elevate flussi di potenza, possono innescare oscillazioni elettromeccaniche. Se non smorzate, queste oscillazioni possono provocare blackout. In questa Tesi, un recente strumento matematico chiamato Dynamic Mode Decomposition (DMD) viene utilizzato per identificare i modi predominanti che caratterizzano queste oscillazioni. Basandosi su questo metodo matematico, un algoritmo implementato in Matlab viene utilizzato su diversi tipi di dati, dove vengono mostrati vantaggi e svantaggi di questo metodo. Inoltre, è presentata un’applicazione per l’identificazione in tempo reale di oscillazioni elettromeccaniche nei sistemi elettrici di potenza. Infine, una generalizzazione del metodo è presentata (OptDMD).
Real-time identification of electro-mechanical oscillations with dynamic mode decomposition
SIMONE, RICCARDO
2018/2019
Abstract
In large interconnected power systems, events such as the opening of transmission lines, connection/disconnection of large loads and high-power flows, may trigger electro-mechanical oscillations. If undamped, these oscillations may lead to blackouts. This Thesis employs a recently developed mathematical tool called Dynamic Mode Decomposition (DMD), to identify the predominant modes that characterize these oscillations. Based on this mathematical method, an algorithm is developed in Matlab and then tested with different types of data. Both advantages and disadvantages of the method used are reported. Lastly, a real-time monitoring tool for large power systems and a generalization of the method (OptDMD) are presented.File | Dimensione | Formato | |
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2019_12_Simone.pdf
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https://hdl.handle.net/10589/151299