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dc.contributor.authorBabakmehr, M.
dc.contributor.authorHarirchi, F.
dc.contributor.authorAl Durra, A.
dc.contributor.authorMuyeen, S.M.
dc.contributor.authorSimões, M.
dc.date.accessioned2019-02-19T04:18:23Z
dc.date.available2019-02-19T04:18:23Z
dc.date.created2019-02-19T03:58:21Z
dc.date.issued2018
dc.identifier.citationBabakmehr, M. and Harirchi, F. and Al Durra, A. and Muyeen, S. and Simões, M. 2018. Exploiting compressive system identification for multiple line outage detection in smart grids.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/74893
dc.identifier.doi10.1109/IAS.2018.8544714
dc.description.abstract

© 2018 IEEE Due to system complexity and structural variations, real time power line outage detection (POD) and localization is a critical and challenging monitoring goal for modern smart grid (SG). Online monitoring of power lines status plays a major role in system-wide cascading blackout prevention. In this work, we aim to address the multiple POD problems by exploiting the compressive system identification (CSI) - a time efficient approach in complex network analysis. We consider a power network (PN) as a single graph and the mathematical formulation of POD is initialized using the DC power-flow model and graph theory concepts. The POD sparse recovery problem (POD-SRP) reported earlier is improved and generalized in case of large-scale multiple outages. The technical challenges from sparse recovery perspective are addressed through developing new SRP solvers. Moreover, a new sparse-based mathematical formulation for POD is termed as 'Binary-POD-SRP' to specifically address the signal dynamic range issue. Finally, using the IEEE standard test-beds, it is discussed how the inherent challenges within large-scale multiple-outages can be solved by applying these new techniques and formulations.

dc.titleExploiting compressive system identification for multiple line outage detection in smart grids
dc.typeConference Paper
dcterms.source.title2018 IEEE Industry Applications Society Annual Meeting, IAS 2018
dcterms.source.series2018 IEEE Industry Applications Society Annual Meeting, IAS 2018
dcterms.source.isbn9781538645369
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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