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Decision tree analysis to identify harmful contingencies and estimate blackout indices for predicting system vulnerability

Aliyan, Ehsan; Aghamohammadi, Mohammadreza; Kia, Mohsen; Heidari, Alireza; Shafie-khah, Miadreza; Catalão, João P.S. (2020-01-01)

 
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https://doi.org/10.1016/j.epsr.2019.106036

Aliyan, Ehsan
Aghamohammadi, Mohammadreza
Kia, Mohsen
Heidari, Alireza
Shafie-khah, Miadreza
Catalão, João P.S.
Elsevier
01.01.2020
doi:10.1016/j.epsr.2019.106036
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2020060841182

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©2020 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/
Tiivistelmä
Cascading failure is the main mechanism for progressing large blackouts in power systems. Following an initial event, it is challenging to predict whether there is a potential for starting cascading failure. In fact, the potential of an event for starting a cascading failure depends on many factors such as network structure, system operating point and nature of the event. In this paper, based on the application of decision tree, a new approach is proposed for identifying harmful line outages with the potential of starting and propagating cascading failures. For this purpose, associated with each trajectory of the cascading failure, a blackout index is defined that determines the potential of the initial event for triggering cascading failures towards power system blackout. In order to estimate the blackout indices associated with a line outage, a three stages harmful estimator decision tree (HEDT) is proposed. The proposed HEDT works based on the online operating data provided by a wide area monitoring system (WAMS). The New England 39-bus test system is utilized to show the worthiness of the proposed algorithm.
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