2,456 research outputs found
Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems
The first-ever Ukraine cyberattack on power grid has proven its devastation
by hacking into their critical cyber assets. With administrative privileges
accessing substation networks/local control centers, one intelligent way of
coordinated cyberattacks is to execute a series of disruptive switching
executions on multiple substations using compromised supervisory control and
data acquisition (SCADA) systems. These actions can cause significant impacts
to an interconnected power grid. Unlike the previous power blackouts, such
high-impact initiating events can aggravate operating conditions, initiating
instability that may lead to system-wide cascading failure. A systemic
evaluation of "nightmare" scenarios is highly desirable for asset owners to
manage and prioritize the maintenance and investment in protecting their
cyberinfrastructure. This survey paper is a conceptual expansion of real-time
monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework
that emphasizes on the resulting impacts, both on steady-state and dynamic
aspects of power system stability. Hypothetically, we associate the
combinatorial analyses of steady state on substations/components outages and
dynamics of the sequential switching orders as part of the permutation. The
expanded framework includes (1) critical/noncritical combination verification,
(2) cascade confirmation, and (3) combination re-evaluation. This paper ends
with a discussion of the open issues for metrics and future design pertaining
the impact quantification of cyber-related contingencies
A Novel Power-Band based Data Segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing Identification
This paper presents a novel power-band-based data segmentation (PBDS) method
to enhance the identification of meter phase and meter-transformer pairing.
Meters that share the same transformer or are on the same phase typically
exhibit strongly correlated voltage profiles. However, under high power
consumption, there can be significant voltage drops along the line connecting a
customer to the distribution transformer. These voltage drops significantly
decrease the correlations among meters on the same phase or supplied by the
same transformer, resulting in high misidentification rates. To address this
issue, we propose using power bands to select highly correlated voltage
segments for computing correlations, rather than relying solely on correlations
computed from the entire voltage waveforms. The algorithm's performance is
assessed by conducting tests using data gathered from 13 utility feeders. To
ensure the credibility of the identification results, utility engineers conduct
field verification for all 13 feeders. The verification results unequivocally
demonstrate that the proposed algorithm surpasses existing methods in both
accuracy and robustness.Comment: Submitted to the IEEE Transactions on Power Delivery. arXiv admin
note: text overlap with arXiv:2111.1050
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