Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Heavy-Tail Analysis of Network Theory-Based Critical Asset Identification Metrics for Bulk Transmission Power Systems

Bittenbender, Erick K. (2020) Heavy-Tail Analysis of Network Theory-Based Critical Asset Identification Metrics for Bulk Transmission Power Systems. Master's Thesis, University of Pittsburgh. (Unpublished)

Updated Version

Download (3MB) | Preview


Large-scale blackouts present a significant threat to the reliable delivery of electricity expected of utilities. Often these blackouts are precipitated on a small set of failures, whether through component failures or operator error as a result of insufficient real-time system awareness. In response, a wide array of power system modeling methods has emerged to identify critical assets in electric power systems. This work seeks to study a select grouping of network theory metrics proposed in literature to identify critical power system assets. In total, two standard network theory metrics and eight “extended” complex network betweenness and degree centrality metrics across six synthetic power systems of varying size will be examined. These extended complex network representations of power systems account for structural (e.g. system impedances and susceptance) and operational (e.g. power flow and line losses) properties of power systems not readily captured by standard network theory metrics. All ten metrics, evaluated for each of the six networks, are calculated and tested for heavy-tailed, and more specifically power-law tail, distributions to determine potential connections to blackout size distributions. These heavy-tail tests have shown scaling parameters for power-law fits less than 2 for extended betweenness metrics, closely matching blackout data. System operation metrics more broadly have also show consistent power-law identification among different network sizes over the five metrics tested. Comprehensive system analysis to determine which metrics are most powerful in identifying mechanisms underlying blackout size distributions is recommended as a primary direction to extend this work.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Bittenbender, Erick K.erb84@pitt.eduerb84
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairReed, Gregorygfr3@pitt.edugfr3
Committee MemberMao, Zhi-Hongzhm4@pitt.eduzhm4
Committee MemberKerestes, Robertrjk39@pitt.edurjk39
Date: 29 July 2020
Date Type: Publication
Defense Date: 27 March 2020
Approval Date: 29 July 2020
Submission Date: 23 March 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 70
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: power system analysis, critical asset identification, graph theory
Date Deposited: 29 Jul 2020 16:27
Last Modified: 29 Jul 2020 16:27


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item