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the computational burden in characterizing multivariate load state space, a linear
sensitivity based method supported by Latin hypercube sampling of homothetic
stress directions was developed for quickly characterizing the multivariate load state
space for various combinations of component unavailabilities. This aided in iden-
tifying the boundary region with respect to post-contingency performance measure
quickly.
The future directions of research include:
Application for other stability problems: The ef
cient database generation
approach can also be applied to other stability problems such as rotor angle
stability, out of step etc. In these problems the performance measure
s trajectory
sensitivities will have to be used to reduce the computational cost in identifying
the boundary region.
'
Optimal placement of Phasor Measurement Units (PMUs): The high informa-
tion content
in the training database generated from the proposed ef
cient
sampling method enables
finding the most important system attributes for power
system
nding
the optimal placement of PMUs and extracting relevant knowledge from those
PMUs for advancing data-driven power system operation and control.
'
is security state monitoring. This concept is highly bene
cial in
Application in the reliability assessment of Special Protection System (SPS):
The main difference between deriving operating rules and SPS logic are:
The SPS logic is automated.
-
The SPS logic is not only limited to critical operating condition detection
with respect to some stability criteria, but also involves automatic corrective
action to safeguard the system against impending instability.
-
design
procedures and failure assessments, there are important questions to be answered
about SPS operations from a
Even though many works exist that correspond to SPS
“
process level
”
'
system view-point
'
, such as:
Are there system operating conditions (topology, loading,
flows, dispatch, and
voltage levels) that may generate a failure mode for the SPS?
fl
Are there two or more SPS that may interact to produce a failure mode?
So the objective of this research will be to develop a decision support tool to
perform SPS failure mode identi
cation, risk assessment and logic re-design from a
'
cient scenario processing method presented in this chapter
has tremendous scope to be used in biasing the sampling process such that SPS
failure modes (including multiple SPS interactions) can be identi
systems view
'
. The ef
ed, risk levels
may be estimated, and accordingly the logic may be re-designed using the ef
cient
decision tree process.
Acknowledgments The author acknowledges Professor James D. McCalley at Iowa State Uni-
versity (Ames, Iowa, USA), Sebastien Henry at RTE-France (Versailles, France), and Samir Issad
at RTE-France (Versailles, France) for their valuable support during the course of this research
project.
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