Environmental Engineering Reference
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Figure 11. Network of power grid systems with the different types of influences between them
the tuple of its criticalities values considering the
types of influence, which determine these criti-
calities. Example of power grid system criticality
tuple is shown in the Table 6.
The following task is to calculate the particu-
lar criticality stipulated by the given type of influ-
ence. We suggest using Bayesian belief networks
(BBN) to evaluate the criticalities of the different
PG systems.
BBNs are very effective for modeling situa-
tions, where some information is already known
and incoming data is uncertain or partially unavail-
able (unlike rule-based or “expert” systems, where
uncertain or unavailable data results in ineffective
or inaccurate reasoning). These networks also offer
where
Crt S
i
)
- the total power grid system
(
org
criticality;
Crt
( )
- particular criticality of
power grid system conditioned by organizational
influence in PG;
Crt
S
fhys
S
( )
- particular critical-
ity of power grid system conditioned by physical
influence in PG;
Crt
( )
- particular criticality
of power grid system conditioned by logical influ-
ence in PG;
Crt
log
S
i
inf
( )
- particular criticality of
power grid system conditioned by informational
influence in PG;
Crt
S
i
soc
( )
- particular criticality
of power grid system conditioned by societal
influence in PG.
Depending on the scale used to evaluate criti-
cality, each PG system could be characterized by
S
Table 6. Example of power grid system criticality tuple
Power Grid
system
Type of influence
Physical
Informational
Geographic
Logical
Organizational
Societal
Criticalities caused by the given type of influence
PG system 1
H
H
M
L
L
L
PG system 2
H
M
M
L
L
H
………..
PG system N
L
H
M
M
M
L
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