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)
Tiedosto avautuu julkiseksi: : 01.01.2022
Catalão, João P.S.
Julkaisun pysyvä osoite on
©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/
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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