The process of a grid’s power monitoring and its system is quite complicated these days because a lot of variables and uncertainties contained in data. In this case, we use the Dominance-based Rough Set Approach (DRSA) to provide a set of rules for determining the state of the system and categorized into according to their level of safety. It helps system control operator to respond effectively according to their state of the system.

1. Presented by :-
Sarvesh s s rawat

2.  Abstract
 Introduction
 basics of Grid Monitoring System
 Empirical study: Electrical Power Grid Monitoring System
 Conclusions
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3.  The process of a grid’s power monitoring and its
system is quite complicated these days because a
lot of variables and uncertainties contained in
data.
 In this case, we use the Dominance-based Rough
Set Approach (DRSA) to provide a set of rules for
determining the state of the system and
categorized into according to their level of safety.
 It helps system control operator to respond
effectively according to their state of the system.
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4.  A smart grid operates by using advanced sensing
technologies, controlling and integrating
exchanges at transmission and distribution levels.
 There are many control actions which require the
presence of an operator who can take decisions
effectively by drawing conclusions from the data .
 Recently, data mining techniques have been
adopted for drawing the conclusion from data that
the system get from intelligent electronic device
(IED) to extract the rules.
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5.  The Dominance-based Rough Set Approach
(DRSA), developed by Greco et al. , is a new
approach in data mining that is very useful for
data reduction and qualitative analysis.
 Dominance based rough set is an extension to
the classical rough set theory that considers the
preference order of the data.
 These decision rules are in the form of logic
statements of the type ‘‘if conditions, then
decision”.
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6.  A data table is in the form of information system
IS = (U,Q,V, f), where
◦ U is a finite set of objects
◦ Q = {q1, q2, . . . ,qm} is a finite set of
attributes/criteria
◦ Vq is the domain of the attribute/criterion q
◦ f:U × Q→V is a total function such that f(x,q) ∈ Vq for
each q ∈ Q
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7.  In our example, U is a set of 20 records. Q is a
set of 9 attributes/criteria representing the
power flowing in the transmission lines, voltage
levels, and status of circuit breaker for 20
cases.
 The set Q is divided into set C of condition
attributes and set D of decision attributes i.e. the
safety level of our system.
 The decision attributes D (possibly singleton)
makes a partition of U into a finite number of
classes.

8.  We can define unions of classes relative to a
particular dominated or dominating class; these
unions of classes are called upward and
downward unions of classes and are defined,
respectively, as
 The statement ∈ means “x belongs to at
least class and ∈ means “x belongs to
at most class
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x tCl ≥
tCl x tCl ≤
tCl

9.  From our data record, the upward union
at least safe level i.e. safe or unsafe or highly
unsafe level.
at least unsafe level i.e. unsafe level or highly
unsafe level.
at least highly unsafe level, the maximum
safety limit.
 The downward union classes are :
system is under safe level.
at most unsafe level ; i.e. unsafe level or safe
level.
,at most highly unsafe level i.e. highly unsafe
level or unsafe level or safe level.
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3Cl ≤
2Cl ≥
3Cl ≥
1Cl ≥
1Cl ≤
2Cl ≤

10. ⊆
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 The P-boundaries of and are defined as
 We define the accuracy of approximation of
and for all and for any P C, respectively, as
tCl≥
tCl≤
tCl≥
tCl≤

11. Cl
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 The ratio defines the quality of approximation of the
classification .
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12.  The end result of the DRSA is a representation of the
information contained in the considered data table in
terms of simple ‘‘if. . ., then. ..” decision rules.
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13.  Their primary function in a grid is to collect data
from various components, instruments or smaller
systems.
 The collected data is then transferred to the control
system centre to process the data and draw certain
conclusions, based on algorithms that are
programmed into the collection of systems data .
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 For applying DRSA we have redefine the values
of each attributes with respect to a certain
metric. In our example we have used range of
attributes.
 Real power values flowing in transmission lines:
below 40% of capacity = low (L)
Between 40% and 70% = normal (N)
Greater then 70 % of capacity = high (H)

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 Bus voltages:
Less then 0.85 pu = low (L)
between (0.85 and 1.05) = medium (M)
more then 1.05 = high (H)
 status of circuit-breakers:
0=open
1=close

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Applying DRSA in the data table we get

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Set of rules generated using DRSA

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19.  In contrast to the classical rough set theory, which
tends to generate too many rules with only a little
cover strength, with the DRSA we seem to be able to
obtain better decision rules.
 The above proposed dominance based rough set
method provides a system such that the operator
would be able to make more intelligent and
supportive decisions on power utilization options and
load shedding.
 It also allows the system operator to diagnose the
problems effectively and to take decisions based on
algorithms so that one can rapidly repair them.
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20. For more information
Contact: Sarvesh Rawat
sss.sarvesh888@gmail.com
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