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Electric Power Systems Research 81 (2011) 414–420
Contents lists available at ScienceDirect
Electric Power Systems Research
journal homepage: www.elsevier.com/locate/epsr
Methodology for allocation of remotely controlled switches in distribution
networks based on a fuzzy multi-criteria decision making algorithm
D.P. Bernardon a,∗ , M. Sperandio a , V.J. Garcia a , J. Russi a , L.N. Canha b , A.R. Abaide b , E.F.B. Daza c
a
UNIPAMPA – Federal University of Pampa, Brazil
UFSM - Federal University of Santa Maria, Brazil
c
AES Sul, Brazil
b
a r t i c l e
i n f o
Article history:
Received 28 July 2010
Received in revised form 1 October 2010
Accepted 4 October 2010
Available online 29 October 2010
Keywords:
Logical-structural matrix
Multi-criteria decision making
Remotely controlled switches
Reliability
Distribution networks
a b s t r a c t
Continuity in power supply for the consumers is a permanent concern from the utilities, pursued with
the development of technological solutions in order to improve the performance of network restoration conditions. Using remotely controlled switches corresponds to one possible approach to reach such
an improvement and giving some convenient remote resources such as the fault detect, isolation and
transfer loads. This paper presents a methodology implemented in a computer programming language
for allocation these devices in electric distribution systems based on multi-criteria fuzzy analysis. The
main contributions are focus on considering the impact of installing remote-controlled switches in the
reliability indexes and algorithm of fuzzy multi-criteria decision making for the switches allocation. The
effectiveness of the proposed algorithm is demonstrated with case studies involving actual systems of
the AES Sul utility located in the south of Brazil.
© 2010 Elsevier B.V. All rights reserved.
1. Introduction
Utilities have concentrate significant efforts in order to improve
the continuity of electrical energy supplied, specially by understanding this desirable conditions associated with customer
satisfaction and with improvement of the amount of energy available to commercial and industrial activities. In addition, such a
movement is indispensable when looking forward expansion and
frequently maintenance on distribution systems [1].
When facing with a contingency situation, to act as soon as possible may result in a minimum affected area. Whenever a fault is
identified at any point of the network, the following procedures
must be performed: identifying the right defect position; isolating
as much as possible the part of the distribution system by opening
normally closed switches; restoring the power supply to consumers
downstream the isolated block; correcting the problem; reoperating the switches to get back to the normal network status.
Currently, a topic that is being frequently discussed is how the
electric power distribution systems will be in the future. In this
sense, the term “Smart Grid” was defined to describe how this
new network should behave, that is in a “smart” or “intelligent”
way. Among the features of a Smart Grid are the ability to carry
out maneuvers in an automated manner (self-reconfiguration) and
∗ Corresponding author. Tel.: +55 5192811760.
E-mail address: [email protected] (D.P. Bernardon).
0378-7796/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.epsr.2010.10.010
high reliability, all with a low operation and maintenance costs. A
survey of the main projects and research related to Smart Grid is
presented in [2].
Automation of distribution systems plays an important role on
reducing the time to implement a service restoration plan with the
installation of remote-controlled switches, mainly by allowing the
consideration of regulation policies. These devices have shown to
be economically viable due to the emergence of a large number of
suppliers of automation equipment and to the new communication
technologies [3].
The use of an effective methodology to allocate remotecontrolled switches is really important for the utilities, since that
procedure is closely related to the restoration time and consequently associated with reliability index. This kind of solution is
not easy to deal with due to its multiple criteria. Most studies
are limited to develop strategies for operation of remotely controlled switches [4,5], without covering the switches allocation.
The research by Asr and Kazemi [6] involves allocation, however a
monocriteria approach is used and the results are limited to small
systems.
This paper deals with the making of computer algorithms to
address the remotely controlled switch allocation problem on the
basis of a Bellman–Zadeh method [7] with fuzzy multi-criteria
analysis, in order to improve the reliability indexes of the distribution systems. This method has proven to be effective in solving
multi-criteria problems and promoting final solutions belonging
to the Pareto objective space [8]. The algorithm can be configured
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D.P. Bernardon et al. / Electric Power Systems Research 81 (2011) 414–420
according to the needs of the utilities, helping in the decision making process. Thus, the proposed system will indicate where the
resources invested by the utility will bring better operative results
concerning the improving in the reliability indexes of distribution
systems, being characterized as a decision support tool for planning
and operating the distribution networks.
The proposed approach has proven its effectiveness on a specific
portion of a real distribution system in the sense of the improvement in the reliability index and also in the reduction of crew
displacements, letting one concludes the relevant economic and
customer satisfaction benefits obtained.
Therefore, the main contributions of this paper are highlighted
as follow:
(a) a new method to calculate the impact on reliability indexes due
to the installation of remotely controlled switches;
(b) a multi-criteria decision making process for solving the remotecontrolled switch allocation problem using the Bellman–Zadeh
method.
Additionally, the algorithms used for load flow and for reliability
indexes computation are also presented, since they are essential for
modeling the problem, considering the analysis of load transfers
and the reliability indexes computation.
2. Algorithm of load flow
A version of the classical backward/forward sweep method algorithm was performed to calculate the load flow in radial distribution
networks developed by Kersting and Mendive [9]. The solution can
only be found iteratively, because the electrical loads are defined
by a constant power, so the current absorbed by the loads depends
on an unknown value of the voltage. The resulting procedure is
described as follows:
1st Stage: it is considered that the voltage in all points of the
feeder is the same as the voltage measured in the substation bar
(flat start). This information can be automatically received by the
remote measurement systems installed at the substations. Do not
consider voltage drops in the branches at this stage.
2nd Stage: active and reactive components of the primary currents
absorbed and/or injected in the system by the electrical elements
are calculated.
3rd Stage: the procedure to obtain the current in all network
branches consists of two steps: (a) a search in the node set is performed adding the current values in the set of branches and (b)
currents from the final sections up to the substation are accumulated.
4th Stage: voltage drops in primary conductors are determined.
5th Stage: from the substation bar it is possible to obtain the voltage
drops accumulated at any other part of the primary network, and,
consequently, the voltage values at any point.
6th Stage: the difference between the new voltage values for all
nodes and the previous values is checked. If this difference is small
enough, the solution for the load flow calculation was found and
the system is said to be convergent. Otherwise, the previous steps
are repeated, from step 2 on, using the calculated voltages to obtain
the current values. Iterations are performed until the difference
found is lower than a threshold. In this paper, a threshold of 1%
was chosen, because it promotes accurate values for the status
variables without requiring too much time to process. At the end
of the procedure, the active and reactive powers, losses, voltages
and currents are defined for all the primary feeder’s branches and
nodes.
415
This load flow method was implemented in the proposed
methodology for analyzing the technical feasibility of the load
transfers, which will impact in the set of candidate points to receive
the remotely controlled switches. The criteria related to the technical feasibility of load transfer through the remotely controlled
switches were considered as constraints. That is, such transfers may
not result in overload of any electrical element, violation of the permissible limits of the protective devices or the permissible voltage
range limits of the primary networks. The constraints assessment is
performed considering the most severe operation scenario, represented by the heaviest load profile, ensuring that the load transfers
can be feasible at any time. The modeling of power load profiles was
performed from typical load curves measured in the concession
area of the AES Sul utility.
3. Algorithm for calculating reliability index
The criteria adopted in the remotely controlled switches allocation was the improvement on the reliability index. For this purpose,
some indicators were chosen; they correspond to the expected values of [10]:
• System Average Interruption Frequency Index:
SAIFI =
total number of customer interruptions
total number of customers served
(/year)
(1)
• System Average Interruption Duration Index:
SAIDI =
interrupted customers × interruption duration
total number of customers served
(h/year)
(2)
• Energy Not Supplied Index:
ENS =
interrupted power
× interruption duration
(kWh/year)
(3)
These indexes can be obtained from the logical-structural matrix
(LSM) [11], which includes the following input data:
• Annual failure rate ();
• Mean time to restore power supply (TR);
• Number of customers served by distribution transformers or primary consumers (N);
• Load, active power, of the distribution transformer or primary
consumers (L).
It is highlighted that time to restore power supply is composed
by:
• Mean time of wait (TW): time interval to respond for the emergency occurred, it is bounded by the knowledge of the existence
of an occurrence and the time taken for the authorization of the
emergency crew to take care of the contingency;
• Mean time to travel (TTr): time interval between the moment of
authorization of the emergency crew until the moment of arrival
at the scene;
• Mean time to repair or service (TS): time interval between the
instant of the emergency crew arrival at the scene until the
moment of restoring power supply, for each occurrence of an
emergency.
Each column of the matrix corresponds to the branches of the
distribution network protected by a specific protective device or
switching equipment. Each row of the matrix corresponds to the
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Table 2
Logical-structural matrix with times vs. failure rate.
Nodes (distribution
transformers or primary
customers)
Fig. 1. Distribution network.
distribution transformers or to primary consumers. In the cells of
the logical-structural matrix, there are initial values of the mean
time to power restoration. In order to define these values, it is
required to analyze how long it takes to power supply restoration for the corresponding consumers (matrix line), when they are
faced with a failure in the distribution network assuming the protective and switching equipments installed on the network (matrix
column).
In the presence of switching equipment, one must evaluate the
possibilities for switching, isolating defects or transferring loads
through these devices. The first possibility is sectionalizing, which
corresponds to the isolation of the segment under failure and other
associated nodes downstream of a NC (Normally Closed) from
nodes upstream. For all these nodes upstream, the mean time to
isolate (TI) for consumers upstream is computed. The second option
is the transfer of the nodes downstream from the NC switch when
upstream faults occur, being considered as mean time to transfer
(TT) for consumers downstream. This transfer depends on the existence of a NO (Normally Open) switch downstream from the NC,
and the feeder that will receive the consumers must have the technical capacity available to receive the loads to be transferred. For
manual switches, the TI and TT also include: mean time of wait (TW)
and mean time to travel (TTr). For automatic switches, the TI and
TT are much shorter, because there are not TW and TTr. Normally
TR > TT > TI.
In the case of protective devices, they interrupt the short circuit
current, not allowing that a defect downstream reaches the nodes
upstream. Thus, these nodes are not affected by the failure and
therefore do not have the power supply interrupted.
To illustrate, the logical-structural matrix for the simplified distribution network of Fig. 1 will be shown. It is assumed that the NO
switch at node 5 is connected to another feeder with the technical
capability to receive loads downstream of the NC switch.
Table 1 shows the construction of logical-structural matrix
for the example in Fig. 1, considering the mean time to
1
2
3
4
5
6
7
8
n m
1
2
3
4
5
6
7
8
i=1
ESAIDI =
Switch,
NC
Fuse,
FU-1
Fuse,
FU-2
TR1
TR1
TT
TT
TT
TT
TT
TR1
TI
TI
TR2
TR2
TR2
TR2
TR2
TI
0
0
0
0
0
TR3
TR3
0
0
0
0
0
0
0
0
TR4
Fuse,
FU-1
Fuse,
FU-2
TR1 ␭1
TR1 ␭1
TT ␭1
TT ␭1
TT ␭1
TT ␭1
TT ␭1
TR1 ␭1
TI ␭2
TI ␭2
TR2 ␭2
TR2 ␭2
TR2 ␭2
TR2 ␭2
TR2 ␭2
TI ␭2
0
0
0
0
0
TR3 ␭3
TR3 ␭3
0
0
0
0
0
0
0
0
TR4 ␭4
j=1
Mi,j · Ni
(4)
NC
n
⎛
⎝
m
i=1
Circuit
breaker,
CB-1
Switch,
NC
where ESAIDI = expected value of system average interruption
duration index (h/year); Mi,j = element in row i and column j of
LSM; Ni = number of consumers for the row i; NC = total number
of customers served; n = number of rows; m = number of columns.
The expected value of ENS is straightforward obtained by replacing the number of consumers in Eq. (4) by its respective load, active
power of the distribution transformers, ignoring the total number
of customers served.
EENS =
Protective and switching equipments
Circuit
breaker,
CB-1
power restoration (TR), isolation (TI) and transfer (TT) for each
device.
One can note that for the outage of the circuit breaker (CB-1) the
total time to restore power for all consumers is computed, except
to those downstream of the NC switch, for which is considered
the transfer time to another feeder. For failures downstream of the
NC switch, the time to isolate the fault for upstream consumers of
the switch and the total time to restore power to its downstream
customers is computed. Regarding the outage of fuses (FU-1 and
FU-2), it only affects its downstream consumers, so the total time
to restore power is computed. The upstream nodes are not affected
by the fault, not suffering interruption, since the fuse is coordinated
to blow before the circuit breaker trips (trip saving scheme).
Then, the values of the matrix are multiplied by the failure rate
of the respective equipment, as shown in Table 2.
The reliability indexes are then calculated from the LSM. To calculate the expected value of SAIDI, the terms of each row of Table 2
are added and then multiplied by the respective amount of consumers in that row, and then the results of all lines are added
together and divided by the total number of customers served, as
follows:
Table 1
Logical-structural matrix to the distribution network of Fig. 1.
Nodes (distribution
transformers or primary
customers)
Protective and switching equipments
⎞
Mi,j ⎠ · Li
(5)
j=1
where EENS = expected value of energy not supplied (kWh/year);
Mi,j = element in row i and column j of LSM; Li = load, active power,
associated to row i (kW); n = number of rows; m = number of
columns.
To obtain the expected value of SAIFI, the process is similar to the
SAIDI, requiring only replacement of the logical-structural matrix
average times (TR, TI and TT) by 1, so are considered only the failure
rates.
n m
ESAIFI =
i=1
M∗
j=1 i,j
· Ni
(6)
NC
where
ESAIFI = expected
value
age
interruption
frequency
of
system
(failures/year);
aver∗ =
Mi,j
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D.P. Bernardon et al. / Electric Power Systems Research 81 (2011) 414–420
element in row i and column j of LSM,
without
considering the mean times; Ni = number of customers for the row
i; NC = total number of customers served; n = number of rows;
m = number of columns.
4. Proposed methodology for allocation of remotely
controlled switches
When observing the recent trends in automation of distribution
networks, the use of remotely controlled switches is pretty convenient. Considering a gradual update of the distribution system,
it becomes necessary to prioritize the points for installing these
equipments in order to obtain the highest return rates for the utility.
Therefore, the main purpose of the proposed methodology is
to define a rank of the best places to allocate pairs of remotely
controlled switches on the feeders of a distribution network: a NC
switch to be installed in the main trunk feeder and a NO switch in
the tie-switch with another feeder. Next, the objective functions
and the constraints should be defined in order to have the whole
problem formulated.
In this study, objective functions were defined to improve the
reliability indexes SAIDI, SAIFI and ENS. The criteria related to the
technical feasibility of load transfer through the remotely controlled switches were assumed as constraints.
That is, such transfers may not result in overload of the electrical elements, violation of the permissible limits of the protective
devices or violate the permissible voltage range limits of the primary networks. The following equations describe all these concepts
and complete the proposed formulation of the considered switch
allocation problem:
Objective functions:
• Minimization of the expected value of SAIDI:
n m
i=1
Min fESAIDI =
j=1
Mi,j · Ni
NC
(7)
• Minimization of the expected value of SAIFI:
n m
M∗
j=1 i,j
i=1
Min fESAIFI =
· Ni
NC
(8)
• Minimization of the expected value of ENS:
Min fEENS =
n
⎛
⎝
i=1
m
⎞
Mi,j ⎠ · Li
(9)
j=1
Constraints:
• Radial network;
• Current magnitude of each element must lie within their permissible limits:
Ii ≤ Ii max
(10)
• Current magnitude of each protection device must lie within its
permissible limits:
Ii ≤ Ij prot
(11)
• Voltage magnitude of each node must lie within their permissible
ranges:
Vj min ≤ Vj ≤ Vj max
(12)
where fESAIDI – ESAIDI function; fESAIFI – ESAIFI function; fEENS –
EENS function; Ii – current at branch i; Ii max – maximum current
accepted at branch i; Ij prot – current limit threshold of the protection device j; Vj – voltage magnitude at node j; Vj min – minimum
417
voltage magnitude accepted at node j; Vj max – maximum voltage
magnitude accepted at node j.
The load flow algorithm checks if the constraints are not violated
for those points related to the installation of remotely controlled
switches, considering the maximum load profile by representing
properly the most severe operation scenario. In order to obtain the
main functions is necessary to adjust the LSM to consider the impact
of remotely controlled switches, as shown in the next section.
4.1. Proposed methodology to consider remotely controlled
switches in reliability index
Defining the most convenient locations for installing remotely
controlled switches (NC in the main trunk feeder and NO in the
tie-switch) in distribution networks involves the calculation of the
reliability indexes several times, once for each pair of candidate
points, in order to verify the values of reduction of the objective
functions (ESAIDI, ESAIFI and EENS) compared to the original configuration.
Thus, the proposed approach for calculating the reliability
index considering the impact of remotely controlled switches
becomes straightforward, since it only changes the cells of the
logical-structural matrix (LSM) affected by these switches without
reconstructing the entire matrix.
For demonstration purposes, consider that the NC and NO
switches of the distribution network of Fig. 1 are remotely controlled. With this assumption, fault isolation and load transfers can
be safely and quickly performed by remotely operation, avoiding
crew travel time and manual procedures.
In the case of failures downstream of the NC remotely controlled switch, the values in the LSM of the nodes upstream of the
equipment must be changed to zero, since the switch prevents the
spreading of the defect in less than 3 min due to the remote sectioning of the feeder. For those defects upstream, it is considered
the mean time of remote transferring (TRT) of the loads, typically
less than 5 min, attributing this value to nodes downstream of the
switch.
Table 3 shows the result of this procedure applied to Table 2,
with the NC and NO switches of the example of Fig. 1 remotely
controlled.
It can be noted that the columns related to failures downstream of the fuses have not changed since the remotely controlled
switches do not have any influence on these failures. As mentioned
before, with the proposed approach, only the LSM cells affected by
remotely controlled switches are changed, reducing the processing
time and making possible to assess real distribution systems. This
greatly simplifies the reliability indexes computation, especially
when numerous alternatives are tested.
4.2. Selection of candidate points for the allocation of remotely
controlled switches
The first procedure that takes place when considering the installation of remotely controlled switches is the definition of the subset
of points which are able to receive these devices. The criterion
proposed in this paper is based on analysis of the tie-switches
between feeders, by identifying for each NO remotely controlled
switch which are the points of the feeder trunk that can receive
the remotely controlled NC switch without causing violation of the
constraints.
All the load transfers are analyzed by a heuristic search
procedure based on the branch-exchange strategy [12], always
maintaining a radial configuration and respecting all constraints
in an attempt to produce new and promising configurations when
observing the defined objective functions.
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D.P. Bernardon et al. / Electric Power Systems Research 81 (2011) 414–420
Table 3
Logical-structural matrix considering the impact of remotely controlled switches.
Nodes
Protective and Switching Equipments
(distribution transformers
Circuit Breaker
Remotely-Controlled Switch
Fuse
Fuse
or primary customers)
CB - 1
NC
FU - 1
FU - 2
1
TR1 λ1
0
0
0
2
TR1 λ1
0
0
0
3
TRΤ λ1
TR2 λ2
0
0
4
TRΤ λ1
TR2 λ2
0
0
5
TRT λ1
TR2 λ2
0
0
6
TRΤ λ1
TR2 λ2
TR3 λ3
0
7
TRΤ λ1
TR2 λ2
TR3 λ3
0
8
TR1 λ1
0
0
TR4 λ4
Cells of the logical-structural matrix (LSM) changed of value due the remotely controlled switches.
In order to reduce the number of alternatives, the analysis
begins with allocation of a NC remotely controlled switch at the
point of the main trunk feeder farthest from the NO remotely controlled switch (tie-switch), repeating this procedure for all locations
on this path towards the considered tie-switch. Moreover, each
analysis takes into account all the constraints previously. Whenever a feasible point is identified, this procedure is interrupted
and all points downstream of this one towards to the tie-switch
are also assumed as feasible, since the load to be transferred is
smaller or equal to the load determined as feasible. After that,
the original configuration is restored and the analysis goes on
with another tie-switch in order to identify all points to receive
remotely controlled switches without violating any of the defined
constraints.
The expected values of improvement of ESAIDI, ESAIFI and EENS
(objective functions) are determined for each pair of candidate
points from this subset. It must be emphasized that this process
is extremely fast because it only changes the LSM cells affected by
remotely controlled switches without requiring the calculation of
the entire matrix again.
4.3. The multi-criteria decision making algorithm
This section presents the algorithm for determining the allocation of the remotely controlled switches, based on multi-criteria
analysis. The main challenge is to define the best places to allocate
different pairs of remotely controlled switches when three objective functions are considered, because there is no absolute solution,
since there are tradeoffs between these functions. For example, a
particular option can have the greatest reduction of ESAIDI, but
other can greatly reduce the ESAIFI and another may be the best
reduction of EENS. A decision making algorithm is the key for support which option should be chosen.
The Bellman–Zadeh method [6] is used as the decision making technique for our approach because of its efficiency in handling
quantitative and qualitative criteria for the problem resolution [13].
The objective functions are replaced by other functions in the form
of fuzzy sets, with the corresponding membership functions given
by Eqs. (13) and (14): one for objective functions to be maximized
and another one for objective functions to be minimized, respec-
tively:
Aj (x) =
Fj (x)
maxFj (x)
,
(13)
,
(14)
x ∈ Dx
Aj (x) =
min Fj (x)
x ∈ Dx
Fj (x)
The maximum intersection between membership functions is
the most attractive configuration – i.e. the best solution that balances the multiple criteria, as shown in Eq. (15).
maxD (x) = max min Aj (x).
x ∈ Dx
x ∈ Dx j=1,...,n
(15)
Fig. 2 illustrates this approach.
Tables 4 and 5 illustrate the application of the Bellman–Zadeh
algorithm for selecting the best options to allocate pairs of remotely
controlled switches, considering there is no violation on the constraints. In this example, five pairs of candidate points for receiving
the remotely controlled switches were considered (Fig. 3).
The values related to the membership functions of fuzzy solutions, for each option, are obtained with the application of Eq. (13)
to maximize the reduction of the reliability indexes, as shown in
Table 5:
According to the proposed method, it is considered as the best
solution the option that has the maximum value of the intersection of the membership functions, given by Eq. (15). In this case,
Fig. 2. Intersection between the membership functions.
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419
Table 4
Results of the analysis for each allocation.
Options
Couple of analyzed switches
ESAIDI reduction (hours-year)
ESAIFI reduction (failures/year)
EENS reduction (kWh/year)
1
2
3
4
5
–
NC-1 and NO-1
NC-1 and NO-2
NC-1 and NO-3
NC-2 and NO-1
NC-2 and NO-2
Reference value
1.8
0.3
1.6
0.4
1.4
1.8
0.2
0.1
0.4
0.2
0.8
0.8
130.70
178.42
161.39
106.04
113.17
178.42
Table 5
Membership functions values and fuzzy solutions.
Options
ESAIDI reduction (hours-year)
ESAIFI reduction (failures/year)
EENS reduction (kWh/year)
Intersection of membership functions
1
2
3
4
5
D1 (x) = 1.00
D2 (x) = 0.17
D3 (x) = 0.89
D4 (x) = 0.22
D5 (x) = 0.78
F1 (x) = 0.23
F2 (x) = 0.13
F3 (x) = 0.50
F4 (x) = 0.25
F4 (x) = 1.00
ıE1 (x) = 0.73
ıE2 (x) = 1.00
ıE3 (x) = 0.90
ıE4 (x) = 0.59
ıE5 (x) = 0.63
min D1 , F1 , E1 = 0.23
min D2 , F2 , E2 = 0.13
min D3 , F3 , E3 = 0.50
min D4 , F4 , E4 = 0.22
min D5 , F5 , E5 = 0.63
Fig. 3. Distribution network.
Fig. 4. Distribution network in a substation of AES Sul.
the best option is “5”, following of the options “3”, “1”, “4” and
“2”, respectively. Thus, after the computer simulations there is a
rank for allocation of remotely controlled switches, according to
the objective functions defined, without violating the constraints
established. So the utility can schedule the investments in the network.
5. Experimental analysis
Case studies have been conducted with the AES Sul power utility, in Brazil, in order to verify the applicability of the proposed
methodology. The network considered involves the metropolitan
area of AES Sul, with ten feeders as shown in Fig. 4
The proposed algorithm takes place by first considering the candidate points of feeders that can receive the NC remotely controlled
switch through the analysis of the technical feasibility of the load
transfer to other feeders, as detailed in the previous section. If the
loads downstream of the point analyzed can be transferred to other
feeders without violating the constraints, the point under consideration can receive a remotely controlled switch.
The results obtained for the pair of switches tested are then
verified, always considering the gains when comparing the initial
configuration and with regard to reduction of reliability indexes
(ESAIDI, ESAIFI and EENS). Finally, it is applied the multi-criteria
decision making method based on Bellman–Zadeh algorithm for
calculate an overall indicator for each pair of switches (NC-NO),
corresponding to the respective value of intersection of the membership functions (Eq. (15)). Therefore, the couple of points that
present the highest indicator value is considered as the best solu-
tion for allocation of the remotely controlled switches. Fig. 5 shows
the points selected for the pair of switches that presented the best
results.
AES Sul has installed in its distribution network one pair of these
remotely controlled switches, allocating them in the points indicated by the tool developed that would present the best results.
Follows the operation strategy of the switches when there is an
outage of the feeder:
Fig. 5. Results obtained from the multi-criteria analysis for the allocation of
remotely controlled switches.
Author's personal copy
420
D.P. Bernardon et al. / Electric Power Systems Research 81 (2011) 414–420
Table 6
Results obtained with the use of remotely controlled switches.
Description
Before installing the remotely controlled switches
After installing the remotely controlled switches
Reduction
Mean time to restore energy
Faults upstream the NC switch
Faults downstream the NC switch
Clients upstream
the switch
Clients downstream the
switch
Clients upstream
the switch
Clients downstream the
switch
1 h36 min
1 h36 min
–
48 min
5 min
43 min
32 min
0 min
32 min
1 h43 min
1 h43 min
–
• Fault downstream of the NC remotely controlled switch: in the
event of a fault, the current values of short-circuit will be flagged
online in the SCADA (Supervisory Control and Data Acquisition)
system. So, it is assumed that the failure occurred downstream of
the NC remotely controlled switch; then, the NC remotely controlled switch is operated automatically to isolate the defect. As
the time to open the switch, from the time when the fault is
detected, is less than 3 min it is considered that the upstream consumers do not suffer any impact due to the failure, for purposes of
accounting the reliability index. For the downstream customers,
the total time to restore energy is computed.
• Fault upstream of the NC remotely controlled switch: in this case,
the fault current values of short-circuit will not be flagged in
the SCADA system. So, it is assumed that the failure occurred
upstream of the NC remotely controlled switch, automatically
operating it to open (NC) and then to close the NO one in order to
transfer consumers downstream of the NC switch to the adjacent
feeder. As the transfer time is on average of 5 min, this time is
considered for the consumers transferred, i.e., downstream the
switch. For the consumers upstream, the total time to restore
energy is computed.
Table 6 shows the results obtained by the application of this
methodology in case of outage of the feeder when considering the
power restoration time.
Finally, it should be noted that a reduction of approximately 30%
on the annual SAIDI index of this feeder is expected, assuming the
number of faults in main trunk feeder.
6. Conclusions
It was demonstrated that the remotely controlled switches significantly affect the reliability indexes (ESAIDI, ESAIFI and EENS),
and the improvement of these indicators is not proportional, with
tradeoffs among them. So, the main contributions of this paper
are the methodology to consider the impact of remotely controlled
switches when computing the reliability indexes and the algorithm
for multi-criteria decision making to allocate these switches.
In addition, the flexibility of the proposed methodology provides a wider comprehension for the computer system developed,
resulting in a useful, reliable and easy-to-use tool for electrical
distribution utilities. For a better evaluation of the software’s performance, case studies were carried out with actual systems and
the results have proved its effectiveness.
Acknowledgements
The authors would like to thank the technical and financial
support of AES Sul Distribuidora Gaúcha de Energia SA, Conselho
Nacional de Desenvolvimento Científico e Tecnológico (CNPq),
Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul
(FAPERGS) and Coordenação de Aperfeiçoamento de Pessoal de
Nível Superior (CAPES).
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Daniel Pinheiro Bernardon was born in Santa Maria, Brazil on the 15th of September
1977. He got his Dr. Eng. degree from Federal University of Santa Maria, in 2007,
and has been a professor of Electrical Engineering at Federal University of Pampa,
since 2008. His research interests include distribution system analysis, planning and
operation, besides working for ten years in Operation of Electric Systems.