ENHANCEMENT OF POWER SYSTEM SECURITY USING PSO-NR OPTIMIZATION TECHNIQUE

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International Journal of Electrical Engineering & Technology (IJEET)
Volume 7, Issue 1, Jan-Feb, 2016, pp.35-44, Article ID: IJEET_07_01_004
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ENHANCEMENT OF POWER SYSTEM
SECURITY USING PSO-NR OPTIMIZATION
TECHNIQUE
P. Sobha Rani
Associate. Professor, Department of E.E.E
Lakireddy Balireddy College of Engineering,
Mylavaram, Andhra Pradesh
K.S.L. Lavanya
Asst. Professor, Department of E.E.E,
Lakireddy Balireddy College of Engineering,
Mylavaram, Andhra Pradesh
ABSTRACT
Maintaining power system security is one of the challenging tasks for the
power system engineers. The security assessment is an essential task as it
gives the knowledge about the system state in the event of a contingency.
Contingency analysis technique is being widely used to predict the effect of
outages like failures of equipment, transmission line etc., and to take
necessary actions to keep the power system secure and reliable. The off line
analysis to predict the effect of individual contingency is a tedious task as a
power system contains large number of components. Practically, only selected
contingencies will lead to severe conditions in power system. The process of
identifying these severe contingencies is referred as contingency selection and
this can be done by calculating performance indices for each contingencies.
This paper presents enhancement of power system security using PSO-NR
optimization technique. The efficiency of proposed algorithm is illustrated by
carrying simulation studies on IEEE 14 bus system .This analysis reveals that
the proposed algorithm is quite simple and efficient for solving OPF problem.
Key words: Contingency, Optimal Power Flow (OPF), Particle Swarm
Optimization (PSO), Severity Index.
Cite this Article: P. Sobha Rani and K.S. Lavanya, Enhancement of Power
System Security Using PSO-NR Optimization Technique. International
Journal of Electrical Engineering & Technology, 7(1), 2016, pp. 35-44.
http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=7&IType=1

P. Sobha Rani and K.S. Lavanya
1. INTRODUCTION
A reliable, continuous supply of electrical energy is essential part of today’s complex
societies. In recent years the power systems are pushed to operate closer to their limits
due to the combination of increased energy consumption and various kinds of
obstructions to extension of existing transmission system. A power system is said to
be secured when it is free from danger or risk. Security is ability of the system to
withstand any one of the pre-selected list of contingencies without any consequences.
Conventional methods [5-7] for contingency analysis involve load flow analysis
which is an iterative method. Various methods like AC load flow and several
performance index (PI) based methods are used for power system contingency
analysis [8]. In conventional methods a power flow solution is required at each
iteration, which is again an iterative method itself. Therefore these methods are not
suitable for online applications due to the large computation time. All these
approaches involve a huge number of AC load flow calculations to determine the bus
voltages and line flows for each contingency [3]. It is a challenging task for today’s
high speed computers and efficient algorithms. Another difficulty is that contingency
analysis always uses approximate fast converging load flow algorithms such as Fast
Decoupled load flow analysis which has poor convergence characteristics when
dealing with heavily loaded power systems. There are other simple techniques such as
most popular DC load flow analysis. The results are acceptable when compared with
standard AC load flow method; however it can only provide the Real Power (MW)
flow under each contingency. Therefore voltage violations and line over loads due to
excessive Reactive Power (Var) flows cannot be detected using this method.
Distribution factors and sensitivity analysis, another method based on linear model
can also be used for this purpose but this method cannot provide accurate solution for
a large power system due to its nonlinearity.
In the past few decades, many stochastic optimization methods have been
developed [9-10], such as Genetic Algorithms (GA) [4], Evolutionary Programming
(EP) [12], and Evolution Strategies (ES). Their applications to global optimization
problems become attractive because they have better global search abilities over
conventional optimization algorithms. Particle Swarm Optimization (PSO) is a newly
proposed population based stochastic optimization algorithm which was inspired by
the social behaviours of animals such as fish schooling and bird flocking [9-10].
This paper presents PSO-NR method for removing line overloads under single
line contingencies. The organization of this paper is as follows. Section II describes
contingency analysis. Section III contains Problem formulation of OPF. Section IV
describes overview of Particle Swarm Optimization. Section V presents the results of
the simulations system.
2. CONTINGENCY ANALYSIS
Contingency Analysis (CA) is one of the "security analysis" applications in a power
utility control center that differentiates an Energy Management System (EMS) from a
less complex SCADA system. Its purpose is to analyze the power system in order to
identify the overloads and problems that can occur due to a "contingency".
Contingency analysis is abnormal condition in electrical network. It put whole system
or a part of the system under stress. It occurs due to sudden opening of a transmission
line, Generator tripping, Sudden change in generation, Sudden change in load value.
Contingency analysis provides tools for managing, creating, analyzing, and reporting
lists of contingencies and associated violations.

Enhancement of Power System Security Using PSO-NR Optimization Technique
CA is used as a study tool for the off-line analysis of contingency events, and as
an on-line tool to show operators what would be the effects of future outages.
 Security is determined by the ability of the system to withstand equipment failure.
 Weak elements are those that present overloads in the contingency conditions
(congestion).
 Standard approach is to perform a single (N-1) contingency analysis simulation.
 A ranking method will be demonstrated to prioritize transmission planning.
 CA is therefore a primary tool used for preparation of the annual maintenance plan
and the corresponding outage schedule for the power system. :
2.1. Contingency Definition
Contingency definition involves preparing a list of probable contingencies.
2.2. Contingency Ranking
Contingency Ranking in descending order is obtained according to the value of a
scalar index, normally called as severity index or performance index (PI). PI is
calculated using the conventional load flow algorithm for individual contingency in
off line mode. Based on the values obtained contingencies are ranked in a manner
where highest value of PI is ranked first.
2.3. Contingency Selection
Contingency selection process consists of selecting the set of most probable
contingencies in; they need to be evaluated in terms of potential risk to the system.
2.4. Contingency evaluation
Finally, the selected contingencies are ranked in order of their severity, till no
violation of operating limits is observed.
3. FORMULATION OF OPTIMAL POWER FLOW PROBLEM
The OPF problem is to optimize the steady state performance of a power system in
terms of an objective function while satisfying several equality and inequality
constraints. Mathematically, the OPF problem can be formulated as given:
Min F(x, u) (1)
Subject to g(x, u) = 0 (2)
h(x, u)  0 (3)
Where x is a vector of dependent variables consisting of slack bus power, load bus
voltages LV , generator reactive power outputs GQ and the transmission line loadings
lS . Hence x can be expressed as given:
]....,...,...,[ 111 nlNGNL llGGLLG
T
SSQQVVPX 
(4)
Where NL, NG and nl are number of load buses, number of generators and
number of transmission line respectively. u is the vector of independent variables
consisting of generator voltages GV , generator real power outputs GP except at the

slack bus 1GP , transformer tap settings T, and shunt VAR compensations CQ .Hence u
can be expressed as given:
]....,....,....,....[ 121 1 NCNGNG CCNTGGGG
T
QQTTPPVVU 
(5)
Where NT and NC are the number of the regulating transformers and shunt
compensators respectively. F is the objective function to be minimized, g is the
equality constraints that represent typical load flow equations and h is the system
operating constraints.
3.1. Objective Function
The severity of a contingency to line overload may be expressed in terms of the
Severity Index, which express the stress on the power system in the post contingency
period. In order to evaluate the security of the power system network a Severity Index
was proposed. The objective function in the proposed OPF was selected as the
minimization of the proposed Severity Index. By minimizing the value of Severity
Index, it can observe an enhancement in the system security [3].For example, in order
to determine the degree of
nmL  .
max
max
mn
mnmn
mn
S
SS
SI


m,n ε NB (6 )
Objective function F =min ( mnSI ) (7)
Where mnSI the Severity Index of line overloads, mnS is the overload flow on
transmission line, is the rated flow on transmission line and NB is the Set of
overloaded lines.
3.2. Constraints
The OPF problem has two categories of constraints
3.2.1. Equality Constraints
These are the sets of nonlinear power flow equations that govern the power system,
i.e.,
0)cos(
1
 
nmmnmnn
l
n
mDmGm YVVPP 
(8)
0)sin(
1
 
nmmn
l
n
mnnmDmGm YVVQQ 
(9)
Where GmP and GmQ are the real and reactive power outputs injected at bus- m
respectively, the load demand at the same bus is represented by DmP and DmQ , and
elements of the bus admittance matrix are represented by mnY and mn .

3.2.2. Inequality Constraints
These are the set of constraints that represent the system operational and security
limits like the bounds on the following.
3.2.2.1. Generation constraints
Generator voltages, real power outputs, and reactive power outputs are restricted by
their lower and upper limits as follows:
m=1,…, NG (10)
maxmin
GmGmGm PPP  , m=1,…, NG (11)
maxmin
GmGmGm QQQ  , m=1,…, NG (12)
Where NG: number of generators
3.2.2.2. Transformer constraints
Transformer tap settings are bounded as follows:
maxmin
mmm TTT  , m=1,…, NT (13)
Where NT: number of regulating transformer
3.2.2.3. Shunt VAR constraints
Shunt VAR compensations are restricted by their limits as follows:
m=1,…, NSVC (14)
Where NSVC: number of Shunt Var Compensators
3.2.2.4. Security constraints
These include the constraints of voltages at load buses and transmission line loadings
as follows:
, m=1,…, NL (15)
Where NL: number of load buses
3.2.2.5. Transmission lines loading
≤ , m=1,…, nl (16)
Where nl: number of Transmission lines
4. OVERVIEW OF PSO
This is a population based optimization method first proposed by Kennedy and
Eberhart in 1995 inspired by social behaviour of bird flocking or fish schooling[11].
The PSO as an optimization tool provides a population based search procedure in
which individuals called particles change their position with time by flying around in
a multi dimensional search space until a relatively unchanging position has been
encountered, or until computational limitations are exceeded. During flight, each
particle adjusts its position according to its own experience(this value is called pbest),
and according to the experience of a neighboring particle. (This value is called gbest),
made use of best position encountered by itself and its neighbor[13-15]. After finding

the best values the particles updated its velocity and position with the following
equations:
)()( 2211
1 k
ibest
k
ibest
k
i
k
i sgrCsprCwVV 
(17)
iteriterwwww *))/()(( maxminmaxmax  (18)
11 
 k
i
k
i
k
i Vss (19)
Where
1C : Cognition Parameter which represents how much the Particle trust its own past
experience;
2C : Social Parameter which represents how much the particle trust the swarm;
21,rr : Random Numbers;
w : Inertia Weight;
k
iV : The Velocity of the agent i in kth
iteration;
:1k
iV Velocity of agent i at (k+1)th iteration
k
is : Current Position of agent i at kth iteration
5. PSO-NR BASED HYBRID METHOD
Basically, the hybrid method involves two steps. The first step employs NR to solve
OPF approximated as a continuous problem and introduced into the initial populations
of PSO. The second part uses PSO to obtain the final optimal solution. In initial
population, all individuals (obtained from NR) are produced randomly. The main
reason for using the NR is that it is often closer to optimal solutions than other
random individuals. In the hybridization of NR and PSO, the NR generates best initial
solutions from random initial solutions and PSO evaluate them by solving the OPF,
which yields to the global optimal solutions for control variables.
The implementation steps of the proposed PSO-NR based algorithm can be
written as follows
Step 1: Input the system data for load flow analysis
Step 2: Assume several contingencies
Step 3: At the generation Gen =0, set the simulation parameters of PSO-NR
parameters and randomly initialize k individuals within respective limits and save
them in the archive.
Step 4: For each individual in the archive, run power flow to determine load bus
voltages, angles, load bus voltage stability indices, generator reactive power outputs
and calculate line power flows.
Step 5: Evaluate the penalty functions
Step -6: evaluate the objective function values and the corresponding fitness values
for each individual
Step 7: Find the generation local best xlocal and global best xglobal and store them
Step 8: Increase the generation counter Gen = Gen+1.
Step 9: Apply the pso-nr operators to generate new k individuals

Step 10: For each new individual in the archive, run power flow to determine load bus
voltages, angles load bus voltage stability indices, generator reactive power outputs,
calculate line power flow
Step 11: Evaluate the penalty functions
Step 12: Evaluate the objective function values and the corresponding fitness values
for each new individual.
Step 13: Apply the selection operator of PSO-NR and update the individuals.
Step 14: update the generation local best xlocal and global best xglobal and store
them.
Step 15: If one of stopping criterion have not been met, repeat steps 3-14. Else go to
step 16
Step 16: checking the limit violation for security constraints. If iterations reached to
its max value then go to else go to step 2.
Step 17: Stop
6. SIMULATION &RESULTS
The proposed approach has been tested on the standard IEEE 14-bus test system as
shown in fig1. The system has five generators at buses 1, 2, 3, 4, 5, and three
transformers with off-nominal tap ratio in lines 6-4, 7-9, 7-8. PSO parameters used for
simulation are summarized in TABLE 1.
Figure 1 Single line diagram of IEEE 14 bus system
Table 1 Optimal Parameter Settings for PSO
Parameter Value
Population size 20
Number of iterations 150
Cognitive constant,c1 2
Social constant ,c2 2
Inertia weight ,w 0.5-1.5

TABLE 2 gives the details of line outage ranking using severity index. When the
line outage is between buses 1-2, without PSO-NR condition, it is observed that the
lines 2-6, 4-12 are overloaded with line flows 106.6. MVA and 41.87MVA
respectively against their line flow limits 65 MVA &32 MVA. In order to rectify the
problem of overflows in lines PSO-NR has been implemented, and then the line flow
limits that are violated under without PSO-NR condition is rectified with the values
12.96 MVA and 23.78 MVA respectively. It is observed that severity index is reduced
by using PSO-NR technique when compared with out PSO-NR severity index.
TABLE 3 presents the setting of control variables for IEEE 14-bus system for
without PSO-NR case and with PSO –NR at different single line outages. From the
results, it is observed that all the control variables are within limits and lines are
operating within the specified line limits by the application of PSO-NR based OPF
algorithm under the occurrence of various severe network contingencies.
Table 2 Line Outage Ranking Using Severity Index
Over
loaded
lines
Line
flow
limit
(MVA)
Line flow (MVA) Severity Index SImn
Ranking
Line
outage
between
buses
Without
PSO-NR
With
PSO -
NR
Without
PSO-NR
With
PSO-
NR
1-2
2-6 65 106.6 12.961 0.64 -0.800
54-12 32 41.87 23.78 0.308 -0.256
1-8 2-6 65 68.211 32.97 0.049 -0.492 3
2-3
3-6 65 70.65 42.25 0.087 -0.35
64-11 32 35.52 9.408 0.11 -0.706
4-12 32 36.8 23.80 0.15 -0.256
2-6 4-12 32 36.92 23.93 0.153 -0.251 1
6-8 4-11 32 35.52 17.60 0.11 -0.45 2
7-9 1-8 65 77.83 23.18 0.197 -0.643 4
Table 3 Control Variables Setting For IEEE 14-Bus System
Control
variables
1-2 outage 1-8 outage 2-3 outage 2-6 outage
Without
PSO-
NR
With
PSO-
NR
Without
PSO-
NR
With
PSO-
NR
Without
PSO-
NR
With
PSO-
NR
Without
PSO-
NR
With
PSO-
NR
P1 1.38 0.228 1.38 0.795 1.38 0.42 1.38 0.579
P2 0.70 0.7 0.70 0.549 0.70 0.559 0.70 0.579
P3 0.28 0.756 0.28 0.384 0.28 0.737 0.28 0.577
P4 0.26 0.733 0.26 0.722 0.26 0.721 0.26 0.721
P5 0.27 0.191 0.27 0.185 0.27 0.189 0.27 0.193
V1 1.07 1.013 1.07 1.049 1.07 1.075 1.07 1.058
V2 1.05 1.009 1.058 1.033 1.058 1.062 1.058 1.048
V3 1.03 1.021 1.03 1.0219 1.03 0.986 1.03 1.10
V4 1.04 1.058 1.049 1.057 1.049 1.058 1.049 1.058
V5 1.02 0.988 1.024 0.988 1.024 0.988 1.0241 0.987
T1 ---- 0.9 ---- 0.9 ---- 0.90 --- 0.9
T2 ---- 0.9 ---- 0.9 ---- 0.90 --- 0.9
T3 ---- 0.95 ---- 0.98 ---- 0.902 --- 1.001

CONCLUSION
This paper presents an improved, efficient and reliable PSO-NR algorithm for solving
Optimal Power Flow problem under occurrence of various single line contingencies.
The proposed method is tested on IEEE-14bus system and the simulation results are
reported.
From the results it can be concluded that Severity index, that is calculated
indicates how much severe a possible line outage is and the severity of each line
outage in the system. The severity index with highest value indicates the severity of
that particular line outage and also indicates that it has got maximum chances of
making system parameters to operate beyond the operating limits. PSO-NR based
Optimal Power Flow algorithm mitigates severity index and shows better performance
under critical conditions. The results show the effectiveness and robustness of the
proposed algorithm in order to solve OPF problem.
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AUTHOR INFORMATION
P.SOBHA RANI is currently working as Assoc. Professor in the
Department of Electrical &Electronics Engineering, Lakireddy
Balireddy college of Engineering, Mylavaram, Andhra Pradesh,
India. Her areas of interest are Distribution systems, Power system
security and distributed generation.
K.S.L.LAVANYA is currently working as Assistant Professor in
the Department of Electrical &Electronics Engineering, Lakireddy
Balireddy college of Engineering, Mylavaram, Andhra Pradesh,
India. She obtained M. Tech from P.V.P Siddhartha Institute of
Technology, Vijayawada, Andhrapradesh, Her areas of interest are
Power System Security, OPF techniques and FACTS.

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