Cluster Computing Environment for On - line Static Security Assessment of large Power Systems

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ACEEE Int. J. on Electrical and Power Engineering , Vol. 4, No. 2, Aug 2013

Cluster Computing Environment for On - line Static
Security Assessment of large Power Systems
Sunitha R1, Sreerama Kumar R.2, Abraham T. Mathew1 and Veeresh P. Kosaraju3
1

Electrical Engineering Department, NIT Calicut, Kerala, India. Email: {rsunitha, atm}@nitc.ac.in
2
King Abulaziz University Jeddah, Saudi Arabia. Email: sreeram@nitc.ac.in
3
Coal India limited, Maharashtra, India. Email: veeresh_kosaraju@yahoo.co.in

Abstract— The increased size of modern power systems
demand faster and accurate means for the security assessment,
so that the decisions for reliable and secure operation planning
could be drawn in a systematic manner. Large computational
overhead is the major impediment in preventing the power
system security assessment (PSSA) from on-line use. To
mitigate this problem, this paper proposes, a cluster computing
based architecture for power system static security assessment,
utilizing the tools in the open source domain. A variant of the
master/slave pattern is used for deploying the cluster of
workstations (COW), which act as the computational engine
for the on-line PSSA. The security assessment is performed
utilizing the developed composite security index that can
accurately differentiate the secure and non-secure cases and
has been defined as a function of bus voltage and line flow
limit violations. Due to the inherent parallel structure of
security assessment algorithm and to exploit the potential of
distributed computing, domain decomposition is employed for
parallelizing the sequential algorithm. Extensive
experimentations were carried out on IEEE 57 bus and IEEE
145-bus 50 machine standard test systems for demonstrating
the validity of the proposed architecture.

scenarios, give operational limits often that are too restrictive
or, in the case when the real time conditions differ to the
reference values, highly conservative [2]. Therefore, these
analyses appear to be inadequate in the new competitive
scenario where there is an uncertainty in predicting the future
operating conditions. This trend has increased the need for
fast and more accurate methods of security assessment [1].
In the new competitive environment, the utilities are forced
to conduct the real time power system security assessment,
in which the security is assessed in real time for a large set of
probable contingencies and transactions [3]. The real-time
analysis could lead to a credible improvement of the utilization
of the available infrastructure at adequate reliability levels
allowing system operators to obtain more realistic operational
guidance in planning preventive and corrective actions aimed
to mitigate the effect of critical contingencies [1-2]. Traditional
sequential computation is inadequate for on-line power
system security analysis as the entire computation should
take, typically less than a few minutes for the information to
be useful [4]. The application of artificial intelligent [5] and
probabilistic [6] based methodologies have been attempted
for obtaining fast but less accurate solution for security
assessment.
Considerable research efforts [7]-[9] have also been
oriented to develop dedicated computer architectures based
on supercomputers or network of workstations for the fast
solution of power system state equations. This method is
applied particularly to on-line power system security
assessment, where it is necessary to predict the impact of
credible contingencies and suggest suitable preventive or
corrective control actions within a few minutes to mitigate
the effects of critical events. In recent years parallel
processing based on distributed systems seems to be a viable
solution to speed up the simulations in order to obtain results
in useful time. Security constrained optimal power flow
solution in a distributed computing environment is proposed
in [8]. In [9] the various functions of security analysis are
mapped on to a network of workstations which work as a
continuous flow of base case conditions. As supporting tools
in developing this activity, the application of TCP/IP based
communication services and web based control architectures
have been recently published in [2].
This work proposed in this paper mainly focuses on power
system static security assessment, contingency screening
and ranking. Contingency screening and ranking is conventionally performed by computing a scalar performance index
(PI), derived from DC or fast decoupled load flow solution for

Index Terms—first term, second term, third term, fourth term,
fifth term, sixth term

I. INTRODUCTION
Modern society critically relies on a securely operated
electric power system for electricity. By nature, a power system is continually experiencing disturbances (contingencies),
such as load changes, outage of generators or other equipment, short circuits, or combination of such events. These
disturbances usually lead to changes in the configuration
and/or state of the power system. Security refers to the degree of risk in a power system’s ability to survive imminent
disturbances without interruption to customer service at any
instant of time [1]. With the initiation of the deregulated electricity market, the system operators are concerned with the
special measures to protect the system against severe contingences and to increase the security margins. These actions are performed by them based on the results obtained
by conducting power system security analysis The calculations required for the power system security assessment are
performed based on the (n-1) criterion that requires the analysis of system behavior and the verification of operational
limits violations for each credible contingency. Traditionally
these analyses are carried out off -line as it requires the solution of system state equations in both static and dynamic
time frame. These off-line analyses referred to as worst case
© 2013 ACEEE
DOI: 01.IJEPE.4.2.1283

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each contingency [10]. These methods generally employ a
quadratic function as the performance index. This makes the
contingency ranking prone to masking problems, where a
contingency with many small limit violations is ranked equally
well with the one in which there are only a few large limit
violations. Also, the selection of weighting factors in the
performance index is found to be a difficult task, as it should
be chosen based on both the relative importance of buses
and branches and the power system operating practice [10].
In addition, majority of the performance indices do not provide
an exact differentiation between the secure and non-secure
states. The conventionally used performance indices were
seen to be calculated separately for line flows and bus
voltages, as the overall performance index defined as the
sum or weighted sum of the scalar performance indices for
bus voltages and line flows could not provide accurate
results.
In Ref. [10], authors have proposed an accurate method
of critical contingency screening and ranking based on
composite security index PI c which is calculated using
Newton Raphson load flow technique. The PI c is defined
based on both bus voltage and line flow limit violations and
it has been demonstrated in [10] that it completely eliminates
the masking problem. It also provides a proper definition of
security in which the secure state is indicated by an index
value of ‘0’, while a value greater than ‘1’ indicates an insecure
state. Index values lying between ‘0’ and ‘1’ indicate the alarm
limit. In this method, the difficult task of selecting the weights
is also completely avoided.
In this paper, a cluster computing environment for online power system static security assessment based on
composite security index PI c is proposed and a prototype is
designed. A variant of the master/slave pattern with only
those tools in the open source domain are used for deploying
the computational engine. The sequential algorithm for
security assessment is parallelized using domain
decomposition. Experimental investigations are carried out
on IEEE 57 bus demonstrate the effectiveness of the proposed
solution.
The outline of the paper is as follows. Formalization of
static security assessment problem is given in section II.
Development of composite security index is given in section
III. A frame work for performing security assessment using
cluster computing environment is presented in section IV.
Deployment of computational engine along with the
definitions of standard performance measures used in parallel/
distributed computing architectures are given in section V.
Experimental results and discussions are presented in section
VI. Finally conclusions are drawn in section VII followed by
references.
II. POWER SYSTEM SECURITY ASSESSMENT
Power system security assessment is associated with the
steady state and dynamic response of the power system to
various disturbances. This process can be divided in to three
67
© 2013 ACEEE
DOI: 01.IJEPE.4.2.1283

sequential activities: i. contingency screening and ranking,
ii. static and dynamic contingency analysis and iii. preventive
and corrective control. The security analysis is performed
according to the (n-1) criterion that requires systems to be
operated so as to withstand all single contingencies [1]. In
this work the first and second activities are mainly considered
as they are known to be the bottleneck in the online
computations.
A. On line Static Security Assessment
The calculations needed for the on-line static security
assessment requires the steady state solution of the power
system state equations in order to identify the voltages in all
network nodes and the power flows in each line in real time.
This real time power flow solution, updated every few
minutes, is adopted as reference in the automatic assessment
of the static security of the system. The limit violations in
bus voltages and line flows identified by computing a scalar
performance index each for bus voltages and line flows. Then
the solution engine automatically studies hundreds of
possible contingencies that would happen on the power
system determining how well the system can withstand them
[2]. The sequence of major steps for on-line power system
static security assessment is as follows:
i. Acquire field data.
ii. A software routine that solves the static power flow
problem is invoked. This is then adopted in contingencies
analysis as base case study for N configuration.
iii. Check, if the network technical limits are violated. If
violated the system is not secure in N configuration.
iv. For each contingency, generate an input file containing
the network data modified by the effect of the considered
contingency.
v. This file is then used by dedicated software routines to
solve the corresponding power flow problem.
vi. Check for each contingency, if the network technical
limits are violated.
vii. Generate alarms in the presence of an expected system
malfunctioning.
In this work, the violations in network technical limits are
identified by computing for base case as well as for each
contingency, the composite security index PI c proposed by
the authors in [10], which is defined as a function of both
power flow and bus voltage limit violations. Development of
composite security index is discussed in the following
section.
III.THE COMPOSITE SECURITY INDEX
In this paper, the composite security index PI c developed by the authors in [10] is used for static security assessment. The composite security index has two components one
for bus voltage and the other for line flow security check.
Two types of limits were defined for bus voltages and line
loadings, namely the security limit and the alarm limit. The
security limit is the maximum limit specified for the bus voltages and line flows. The alarm limit provides an alarm zone

Full Paper
adjacent to the security limit, which gives an indication
of closeness to limit violations. The alarm zone also provides
a flexible means of specifying the cut-off point for contingency
selection based upon numerically ranked security index [10].
It is also possible to treat the constraints on the bus voltage
and the line flows as soft constraints, thereby the violation
of these constraints, if not excessive, may be tolerated for
short periods of time.
The system is considered insecure if one or more bus
voltages or line flows exceed their security limit. If one or
more bus voltages or line flows exceed their alarm limit without
exceeding their security limit, the system is considered to be
in the alarm state. If none of the voltages or line flows violates
an alarm limit, the system is considered secure. This is
indicated by an index value of ‘0’.
It is assumed that the desirable voltage at each bus i is

where | Pj | is the absolute value of the power flow through
the line j.
The normalization factor for each line j, is defined in (4) as

g

p,j



| PP , j |  PF

,j

(4)

B a se M V A

For an N-bus, M line system, there are (N+M) dimensional
normalized limit violation vectors of both bus voltages and
line flows. In multi-dimensional vector space these limit
violation vectors form a hyper-box and approximating the
hyper-box by a hyper-ellipse inscribed within, a scalar valued
index named as composite security index PI c [10] can be
formed. The is defined in (5) as;
(5)

d

known and is represented as Vi . The upper and lower alarm
limits and security limits of bus voltages are represented
u

l

u

where n is the exponent used in hyper ellipse equation. From
the definition of composite security index, the system is said
to be in one of the three states as follows.

l

as Fi , Fi , Vi , Vi respectively. The normalized upper and
lower voltage limit violations of each bus voltage Vi , beyond

 Secure state if the PI c  0

the alarm limits are defined as in (1).

V

 F iu
;
V id

d

u
v ,i



d

u
v ,i

 0 ;

d

l
v ,i

F il  V

Vid

d

l
v ,i

 0 ;

i

if

if
V

i

V

if
V

i

 Fi

 F iu

i

;

if

 Alarm state if 0  PI c  1

i

V

u

Insecure state if PI c  1
The contingencies can be accordingly ranked in
descending order of severity based on PI c . It is also possible
to provide precise information about the buses and/or the
lines in which the limit violations occurred so that proper
control actions can be taken, without doing a detailed
contingency analysis [10].

(1)
i

 Fi

l

 F il

For each upper and lower limit of bus voltages, the
IV. POWER SYSTEM STATIC SECURITY ASSESSMENT USING
CLUSTER COMPUTING FRAMEWORK

normalization factor g v ,i is defined in (2).

g
g

u
v ,i

l
v ,i



V

u



i

F

l

V

(2)

i



i

To run the on-line security assessment algorithm
described in the previous section, a framework based on
cluster computing is proposed in this section, utilizing a
hierarchical variant of the master/slave pattern that exploits
the hierarchical topology of interconnected computers, such
as a network of clusters that can assure high reliability,
flexibility, high degree of scalability, and fault tolerance [11].
To reduce the execution time needed for security analysis,
a concurrent algorithm based on domain decomposition is
adopted, instead of functional decomposition that does not
guarantee good performance [12]. In this method the whole
job is divided in to similar tasks, each one assigned to a
different processor. Since each task coincides with the
sequential execution of the analysis of a single contingency,
only minor modifications are necessary to the sequential
algorithm discussed in section II. The activity diagram in Fig.
1 shows the details of the adopted approach.
The base case and a number of contingencies are analyzed to evaluate the security of the electrical grid. For each
case a “slave” task is created, which does the power flow
solution for the electrical grid, calculates the composite

i

d

V



u

F
V

l
i

d
i

For power flows, the limit violation vectors d p and the
normalization factor g p are defined in similar way. Since
only the maximum limits are required to be specified for the
power flow through each line, two types of upper limits are
specified for each line, say the alarm limit PF and the security
limit PP . The security limit is the specified maximum limit of
the power flow through the line. The normalized power flow
limit violation vectors for each line j can be defined as in (3).

d
d

| P j |  PF , j
;
Base MVA

p,j



p,j

 0;

if

if

| P j |  PF , j

(3)

| P j |  PF , j

© 2013 ACEEE
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Figure 1. Activity diagram for the Parallelized Security Algorithm

security index PI c using (5). The critical contingencies are
then ranked according to severity based on, and eventually
give alarms.

user to remotely view live or historical statistics for all
machines that are being monitored.
A. Programming Tools Used
As the principal goal of having the cluster is to parallelize
the computation on different machines, protocols must be
installed for message-passing for distributed memory
applications. In this work, Scilab equipped with Parallel Virtual
Machine (PVM) is used for programming on the cluster. Scilab
is a numerical computational package developed by
researchers from the INRIA and the École nationale des ponts
et chaussées (ENPC) [14].
The PVM computing model enables a collection of
heterogeneous computer systems to be viewed as a single
parallel virtual machine [15]. PVM transparently handles all
message routing, data conversion, and task scheduling across
a network of incompatible computer architectures. The
application is programmed as a collection of cooperating
tasks. Tasks access PVM resources through a library of
standard interface routines. These routines allow the initiation
and termination of tasks across the network as well as

V. DEPLOYMENT OF COMPUTATIONAL ENGINE
The computational engine is deployed on an architecture
consisting of Intel® Xeon® CPU’s operating at 2.33 GHz, 4096
MB of static RAM. Each of the Xeon CPU is a quad core,
implying for every CPU incorporated into the architecture
there are 4 effective processors. All the CPU’s are connected
by a fast Ethernet local area network and are running on
CentOS Linux. Only those tools in the open source are used
to deploy the computational engine.
A cluster of workstations is formed using the Rocks
cluster distribution [13]. The cluster of workstations (COW)
can be further extended to cloud computing, as it is also
equipped with Eucalyptus to facilitate deploying them as
part of into Amazon EC2 based clouds. Ganglia is a scalable
distributed system monitor tool for high-performance
computing systems such as clusters and grids. It allows the
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communication and synchronization between tasks [16]. The
PVM software provides a unified framework in which parallel
algorithms can be executed in an efficient and straightforward
manner using existing hardware.

between ‘0’ and ‘1’, the system is in the alarm state.
Experimental investigations are conducted by varying the
number of processors used for computational engine in order
to evaluate the reduction in the computation time for the
analysis and the performance measures are analyzed. The
results obtained for the test system are presented in the
following sub-section.

B. Performance Measures
Cluster computing involves the execution of a computer
program utilizing multiple computer processors concurrently
instead of using one processor. In its simplest form, the most
obvious benefit of using this architecture is the reduction in
execution time of the program. To measure the performance
of the proposed computational engine, standard definitions
of two types of performance parameters viz. speed up factor,
and computational efficiency [12].
The speedup refers to how much a parallel/distributed
algorithm is faster than a corresponding sequential algorithm
and is defined as

Sp 

T1
Tp

A. IEEE 57 bus test system
The proposed method is applied to IEEE 57-bus test system. The system consists of 57 buses, 72 transmission lines
and 8 transformers. The system data and single line diagram
for IEEE 57 bus test system has been obtained from [17]. In
order to define the composite security index, ±7% and ±10%
of the desired bus voltage is taken as the alarm limit and
security limit respectively, for each bus. For the line flows,
80% of the thermal limit is chosen as the alarm limit [10].
The contingencies for which a security breach is observed
are tabulated in Table I. Column 1 represents different line
outage cases. For example, L 52-53 represents an outage of
line connected between bus numbers 52 and 53. In Column 2,

(6)

where p is the number of processors, T1 is the execution
time of the sequential algorithm on one processor and TP is
the execution time of the parallel algorithm with p processors.
SP therefore describes the scalability of the system as the
number of processors is increased. Ideal speed up is p when
using p processors, i.e. when the computations can be divided
in to equal duration processes with each process running on
one processor, with no communication overhead.
The efficiency is a performance metric that describes the
fraction of the time that is being used by the processors for a
given computation. It is defined as

Ep 

S
T1
 p
p Tp
p

the composite security index PI c computed for the
corresponding contingency case is presented and the
security status is shown in column 3. In this case line outages
L 52-53 and L 14-15 are found insecure for which the value is
greater than ‘1’.
TABLE I. CONTINGENCY RANKING FOR IEEE 57 B US TEST SYSTEM

Contingency
(Line Outage)
(1)
L 52-53
L 14-15
L 25-30
L 1-17
L 5-6
L 1-2
L 2-3
L 4-6
L 6-8

(7)

It is a value, typically between zero and one, estimating
how well-utilized the processors are in solving the problem,
compared to how much effort is wasted in communication
and synchronization.
VI. EXPERIMENTAL RESULTS
The effectiveness of the proposed computational engine
for on-line static security assessment is demonstrated
through experimental investigations carried out on IEEE 57
bus standard test system and a large IEEE 145 bus 50 machine
system. The experiments involved the simulation of credible
contingencies such as line outages for each test system for
base load condition. Security assessment has been carried
out as in [10] by computing the composite security index for
the contingencies considered. For programming on the
cluster, Scilab equipped with Parallel Virtual Machine (PVM)
is used.
For different contingencies, the composite security
indices are computed as per (5) and are then ranked in the
order of their severity. According to this index, the insecure
cases are easily identified as those with values greater than
‘1’ and the secure cases are defined as a value of ‘0’ and can
be excluded from the contingency list. If the index value is
© 2013 ACEEE
DOI: 01.IJEPE.4.2. 1283

Com
posite
Security Index
(2)
8.714
3.925
0.9128
0.5487
0. 0531
0
0
0
0

Security Status
(3)
Insecure
Insecure
Alarmstate
Alarmstate
Alarmstate
Secure
Secure
Secure
Secure

For the line outages L 25-30, L 1-17, and L 5-6 the system
is found to be in the alarm state with indices between ‘0’ and
‘1’. For all other contingencies the system is found secure
with an index value of ‘0’. The remaining contingency cases
which are actually secure are not shown in the Table.
The total execution time or turnaround time taken for a
single processor architecture and multi-processor
architecture with number of processors (P) in increments of
two is tabulated in columns 2 of Table II, for both
programming tools. The computational times are arrived at
by performing the computation several times and the average
has been taken. Percentage reduction in execution time is
also analyzed and tabulated in column 3.
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TABLE II. EXECUTION TIME

P
(1)

TAKEN FOR

E xecu tion
Tim e i n secon ds

TABLE III. CONTINGENCY R ANKING

IEEE 57 BUS SYSTEM

% redu ction i n
ex ecuti on tim e

S l.
No

O uta ge

(3)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43

L 119-128
L 121-125
L 121-127
L 125-129
L 102-117
L 134-145
L 141-145
L 108-121
L 139-141
L 134-144
L 136-145
L 17-59
L 72- 11 2
L 27-75
L 67- 12 4
L 65-66
L 141-115
L 6-7
L 142-131
L 24-76
L 136-115
L 131-132
L 120-125
L 36-99
L 122-125
L 25-27
L 142-144
L 136-142
L 137-145
L 142-143
L 66-69
L 73- 10 5
L 74- 10 6
L 119-120
L 66- 11 1
L 1-6
L 2-6
L 7-104
L 33- 11 0
L 1-2
L 120-128
L 6- 12
L 144-145

1
2
4
6

(2)
44.639
24.392
15.498
12.682

0
45.4
65.3
71.6

8
10

8.98 2
7.09 6

79.9
84.1

11

6.40 4

85.7

It has been shown that, for IEEE 57-bus test system, the
turnaround time required for complete static security
assessment with a simulation engine based on single
processor architecture is 44.639 seconds. If multi-processor
architecture is used for computation, the turnaround time
gets considerably reduced. For example with 6 numbers of
processors the total execution time obtained is 12.682 seconds
with a reduction in computation time of 71.6 % with respect
to the single processor architecture.
B. IEEE 145 bus 50 Machine system
In order show the effectiveness of the proposed
computational engine to reduce the computational overhead
of large power systems, cluster computing approach was also
applied to IEEE 145 bus 50 machine test system [17] for static
security assessment. The system consists of 453 transmission
lines including 52 fixed tap transformers.
Static security assessment has been carried out by
computing the composite security index for post contingency
conditions taking line outages as contingencies. For each
bus, ±7% and ±10% of the desired bus voltage is taken as the
alarm limit and security limit respectively. For the line flows,
80% of the thermal limit is chosen as the alarm limit.
Contingency cases are ranked in the decreasing order of
severity based on the composite security index and are shown
in Table III. It can be observed that around 43 contingencies
are identified as insecure with index values greater than ‘1’.
The contingency cases that are actually in secure state or in
alarm state are not shown in the Table.
The total computational time for a single processor and
by varying the number of processors in increments of two is
tabulated in Table IV. Explanations can be made similar to
that of IEEE 57 bus system.
From Table IV it can be observed that, for large IEEE 145
bus system, the complete static security assessment took
1343.2 seconds with single processor architecture. The
execution time gets considerably decreased with the multiprocessor architecture as shown in Table IV. Percentage
reduction in computation time is analyzed and is provided in
column 3 of Table IV. With an 11 processor architecture it can
be observed that the computation time get decreased by
87.8% with respect to single processor architecture. So the
proposed computational engine is scalable.

© 2013 ACEEE
DOI: 01.IJEPE.4.2. 1283

TABLE IV. EXECUTION

P
(1)
1
2
4
6
8
10
11

71

FOR

IEEE 145 B US TEST SYSTEM

Com posite
secur ity index
PI C
5811
5807
5770
5770
3745
51 1.0576
22 7.1176
18 2.1201
15 7.4557
13 0.9143
11 8.8362
11 0.4603
99.1629
93. 089
80.0822
78. 857
58.9581
38.4065
32.8291
31.3636
27.5093
21.7238
19.8099
15.0637
14.9502
13.1515
12.6587
10.7621
10.1162
8.1974
8.0106
6.5935
6.0114
5. 743
3.0649
2. 301
2.2583
2.0933
1. 764
1.7066
1.4103
1.2981
1.1321

TIME TAKEN FOR

S ec urity
S tatus

I nsecur e

IEEE 145 BUS SYSTEM

Execution
Time in seconds

% reduction in
execution time

(2)
1343.18
746.21
408.6
317.82
232.46
181.43
163.41

(3)
0
44.4
69.6
76.3
82.7
86.5
87.8

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utilization of processor’s capacity in solving the problem
compared to the time wasted for communication and synchronization. It can be concluded that depending upon the
size and complexity of the power system, more number of
processors can added, so that the security status can be
obtained within the stipulated time, and proper control actions can be suggested to bring the system back to the secure state.

A. Performance Comparison
The performance of the computational engine for IEEE 57
bus and IEEE 145 bus test systems are analyzed by computing
the performance parameters viz. speed up and computational
efficiency as per (6) and (7) respectively. The variation of
speedup and computational efficiency with the number of
processors, are given in Fig. 2 and Fig. 3 respectively for
both tests systems.
The speedup factor SP is increasing consistently with the
increase in the number of processors for both test systems.
This implies that the performance improvement is guaranteed
and hence the proposed system is scalable. It can be
observed from Fig. 3 that, as the number processors increased
above 6 the computational efficiency also get improved.

VII. CONCLUSION
A scalable solution based on high performance computing
clusters is presented to address the requirement for faster
and accurate methodologies for real time power system
security analysis. The developed composite security index
based on both power flow and bus voltage limit violations
acts as the tool on-line static security assessment.
Investigations were carried out different standard test
systems, to validate the proposed method. The preliminary
investigations reveals that high performance computing
engine based on COW’s is a viable scalable solution for
performing accurate and fast security analysis. The efficiency
of the engine increases with the complexity of the system.
This is a first step towards realizing the full potential of cluster
computing architecture for power system static security
assessment. The COW, being equipped with Eucalyptus, can
be integrated into private or public clouds making use of the
computing resources. As the cloud computing has the
potential to furnish on-demand a dynamically variable
computational power without affecting the accuracy, the
research in this direction is under progress. The work can
also extended for dynamic security assessment for predicting
the impact of critical contingencies on the system.

Figure 2. Speed up curve for IEEE 576 bus and IEEE 145 bus
systems

REFERENCES
[1] K. Morison, “Power system security in the new market
environment: future directions,” Proc. IEEE-PES Winter
Meeting, pp. 78–83, 2000.
[2] Quirino Morante, Alfredo Vaccaro, Domenico Villacci and
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d’Ampezzo, Italy, Aug.22-27, pp.240-246, 2004.
[3] Neal J Balu et. al., “On-line power system security analysis,”
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[7] Daniel J Tylavsky and Anjan Bose, “Parallel processing in
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Systems, Vol. 7, no. 2, pp. 629-638, May 1992.

Figure 3. Computational Efficiency Curve for for IEEE 576 bus and
IEEE 145 bus systems

It can also be observed that as the computational complexity increases as in the case of large IEEE 145 bus system,
speed up and computational efficiency are also increased.
This ensures the scalability of the system and the maximum
© 2013 ACEEE
DOI: 01.IJEPE.4.2.1283

72

Full Paper
[8] O R Saavedra, “Solving the security constrained optimal power
flow problem in a distributed computing environment,” IEEE
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[10] Sunitha R, Sreerama Kumar R., Abraham T. Mathew, “A
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[11] V C Ramesh, “On distributed computing for on-line power
system applications” Electrical power and energy systems
Elsevier science limited, Vol. 18, no. 8, pp.527-533, Mar. 1996.

© 2013 ACEEE
DOI: 01.IJEPE.4.2.1283

[12] G Aloisio, M l Scala, and R Sbrizzai, “A distributed computing
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[13] University of California, “Rocks Base Roll: Users Guide,”
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/cermics.enpc.fr/scilab_new/site/Liens/intro/intro.html
[15] Al Geist et.al. PVM: Parallel Virtual Machine A Users- Guide
and Tutorial for Networked Parallel Computing, The MIT
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[16] Michael J Quinn, Parallel Programming: Using MPI and
OpenMP – Tata McGraw -Hill Edition 2003
[17] Power System Test case Archive. [Online]. Available: http://
www.ee.washington.edu/research/pstca/

73

Cluster Computing Environment for On - line Static Security Assessment of large Power Systems

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