International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 4, Issue 12, December 2015
ISSN 2319 - 4847
Coordinated Design of PSS and STATCOM
based Power Oscillation Damping Controller
using MOL Algorithm
Sangeeta Nayak1 , Sangram Keshori Mohapatra2
1
Department of Electrical Engineering C.V. Raman College of Engineering
Bhubaneswar, Odisha, India
2
Department of Electrical Engineering C.V. Raman College of Engineering
Bhubaneswar, Odisha, India
ABSTRACT
In this paper power-system stability enhancement by simultaneous tuning of Power System Stabilizer (PSS) and Static
Compensator (STATCOM) based damping controllers is thoroughly investigated. The power system stabilizer (PSS) input
signal can be either speed deviation  or active power Pa are considered for the proposed analysis. The design problem of
the proposed controller is formulated as an optimization problem, and MOL algorithm is employed to search for the optimal
controller parameters. The performance of the proposed coordinated control of  based PSS with  based STATCOM is
 based STATCOM controller under different disturbances and loading conditions
 based PSS with  based STATCOM
controller better than coordinated control of Pa based PSS with  based STATCOM controller of the proposed power
compared with
Pa
based PSS with
for SMIB and multi-machine power system. It is verified that coordinated
system in term of power system stability improvement.
Keywords- MOL Algorithm, STATCOM, Power System Stabilizer, Multi Machine Power System
1.INTRODUCTION
Power system stability and security are important factor for power system operation [1, 2]. The low frequency
oscillations in the range of 0.1-2 Hz observed in large power systems and their connection, which has poor damping in
a power system. The Power System Stabilizers (PSS) has been widely used for damping oscillations and increasing the
stability of power system. However, PSS may not be able to provide the required damping in modern complex power
systems. Generally, it is important to recognize that machine power parameters changes with loading, making the
machine behavior quite different at different operating conditions. Hence, PSS should provide some degree of
robustness to the variation in system parameters, loading condition and configurations. H∞ Optimization techniques
have been applied to robust PSS design problem [3]. However, the order of the H∞ based stabilizer is as high as that of
the plant. This gives rise to complex structure of such stabilizers which reduces their applicability. A comprehensive
analysis of the effects of the different conventional PSS parameters on the dynamic performance of the power system
was presented in [4]. It is shown that the conventional PSS provide satisfactory damping over a wide range of system
loading conditions [5]. Although PSS provide supplementary feedback stabilizing signals, they suffer a drawback of
being liable to cause great variations in the voltage profile. The recent advances in power electronic technologies have
made the application of FACTS devices very popular in power systems. Most FACTS devices are installed on
transmission lines far away from any generator and their purposes are mainly for reasons other than increasing the
damping of low frequency oscillations. A supplementary controller may be designed for each FACTS device to increase
the damping of certain electromechanical oscillatory modes (inter-area modes), while meeting the primary goal of the
device. Since electronic devices are not directly involved with electromechanical oscillations and the generator signals
are not available locally, the damping controller design is not as straightforward as those of the PSS.
The interaction among PSS and FACTS based controllers may enhance or degrade the damping of certain modes of
rotor’s oscillating modes. To improve overall system performance, many researches were made on the coordination
between PSS sand FACTS power oscillation damping controllers [6-12].Also, the controllers should provide some
degree of robustness to the variations loading conditions, and configurations as the machine parameters change with
operating conditions. A set of controller parameters which stabilize the system under ascertain operating condition may
no longer yield satisfactory results when there is a drastic change in power system operating conditions and
configurations [13].The problem of PSS and FACTS controllers parameter tuning is a complex exercise as
Volume 4, Issue 12, December 2015
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Volume 4, Issue 12, December 2015
ISSN 2319 - 4847
uncoordinated local control of FACTS devices and PSS may cause destabilizing interactions. In this Paper, the
coordinated design of PSS and STATCOM controller is presented. The large numbers of conventional techniques have
been reported in the literature pertaining to design problems of lead-lag (LL) controller structure namely the eigenvalue
assignment, mathematical programming, gradient procedure for optimization, and also the modern control theory.
Unfortunately, the conventional techniques are time consuming as they are iterative and require heavy computation
burden and slow convergence. In addition, the search process is susceptible to be trapped in local minima, and the
solution obtained may not be optimal. Many optimizing liaisons (MOL) algorithm is the simplified form of particle
swarm optimization (PSO) algorithm. PSO algorithm was first developed in 1995 by Kennedy and Eberhart [14].In this
paper MOL algorithm used to find out the optimal controller parameter.
2.POWER SYSTEM UNDER STUDY
A.Single-Machine infinite-bus power system with PSS and STATCOM
The Single-Machine Infinite-Bus (SMIB) power system with PSS and STATCOM shown in single line diagram as
shown in Fig.1 is considered at the first instance in this study. The simulation model of SMIB power system with PSS
and STATCOM controller are considered by taking all the relevant parameters are taken as reference [8, 9].
Generator Bus-1
I
PSS
VT
Bus-2
T1
Bus-3
Tr. line
PL
PL1
VSTATCOM
VB
T2
Shunt
FACTS
Devices
Load
STATCOM
Figure1.Single-machine infinite-bus power systems with PSS and STATCOM
3.THE PROPOSED APPROACH
A.Structure of STATCOM based damping controller
The structure of STATCOM based damping controller is shown in Fig.2. The STATCOM uses a lead-lag structure and
acts as a controller to regulate the voltage signals VSTATCOM_ref. Each structure consists of a gain block, a signal washout
block and two-stage phase compensation block and a sensor delay block. The phase characteristic to be compensated
changes with the system conditions, therefore a characteristic acceptable for a range of frequencies (normally 0.1 to 2.0
Hz) is sought. This may result in less than optimum damping at any one frequency. The required phase lead can be
obtained by choosing appropriate values of time constants T1S , T2 S , T3S , T4 S .The stabilizing gain K S determines the
amount of damping introduced and, ideally, it should be set to a value corresponding to maximum damping. Time
delays can make the less damping features. Recently there is a growing interest in designing the controllers in the
presence of uncertain time delays
[17].
Input
signal
D
KS
sTW
1 sTW
Delay
Gain
Block
Washout
Block
1 sT1S
1 sT2S
1 sT3S
1 sT4S
Two stage
lead-lag Block
max
VSTATCOM
_ ref
VSTATCOM
+

Output
+
min
V STATCOM
__ ref
VSTATCOM _ ref
Figure2. Structure of STATCOM based controller
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B.Structure of the power system stabilizer
The PSS includes an amplification block, a signal washout block, a lead-lag block. The lead-lag block provides a proper
phase-lead characteristic to compensate for the phase lag between the generator electrical torque and the exciter input.
The PSS input signal can be either speed deviation  or active power Pa .The output signal of the PSS is the signal
VS which is used as an additional input to the excitation system block. The structure of the PSS controller is presented
in Fig.4.
VSmax
VS
1  sT1P
1  sT3 P

sTWP
K PS
1  sT2 P
1  sT4 P
1  sTWP
Input
Output
min
Gain
VS
Washout
Two-stage
block
block
lead-lag block
Figure3. Structure of power system stabilizer
C . Objective function
The washout function the value of washout time constant is not very critical and may be in the range 1 to 20 s [14].In
the present analysis, wash out time constant TW = TWP =10s are used. The gains ( K PS and K S ) and the time constants
( T1S ,
T2 S , T3S , T4 S , T1P , T2 P , T3P , T4P ) are to be determined in lead-lag controllers and the time constants( T1P ,
T2P , T3P , T4 P
) are to be determined . It is worth mentioning that the PSS and STATCOM-based controllers are
designed to damp the power system oscillations after a disturbance. In the present study, an integral time absolute error
of the speed deviation is taken as the objective function. The objective function is expressed as:
t  t sim
J 

(1)
|   | t  dt
t 0
where,  is the speed deviation and
t sim is the time range of the simulation.For objective function calculation, the
time-domain simulation of the power system model is carried out for the simulation period. It is aimed to minimize this
objective function in order to improve the system response in terms of the settling time and overshoots. The problem
constraints are the STATCOM controller parameter bounds. Therefore, the design problem can be formulated as
optimization problem
Minimize J
(2)
Subject to
Ki
where
min
 Ki  Ki
max
, Ti
min
 Ti  Ti
max
K imin and K imax are the lower and upper bounds of all the controllers (STATCOM and PSS) and Ti min and
Ti max are the lower and upper bounds of the time constants of all the controllers.
4.OVERVIEW OF MANY OPTIMIZING LIAISONS (MOL) ALGORITHM
Many optimizing liaisons (MOL) algorithm is the simplified form of particle swarm optimization (PSO) algorithm.
PSO algorithm was first developed in 1995 by Kennedy and Eberhart [14]. Initially the PSO algorithm was introduced
for simulating the behaviour of bird flock. Latter the PSO algorithm was simplified and applied to the individual
particles (bird) which were actually involved in performing the optimization. In PSO algorithm, all the particles are
placed at random position and are supposed to move randomly in a defined direction in the search space. Each
particle’s direction is then changed gradually to insist to move along the direction of its best previous positions of and
its peers, searching in their locality to discover even a new better position with respect to some fitness
measures

f :  n  .

n
Let X   be the position of a particle and V be its velocity. Both the initial velocity and position of the particle
are chosen randomly and updated iteratively. The formula for updating the velocity of the particle is given by [15].






V  wV   P RP ( P  X )   G RG (G  X )
(3)
In the above formula w   is a user defined behavioural parameter termed as inertia weight which controls the

number of repetition in the velocity of particle.
Volume 4, Issue 12, December 2015

P and G are the best positions of particle and swarm respectively.
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
ISSN 2319 - 4847

P and G are weighted by the stochastic variables RP , RG ~ U (0,1) .  P ,  G are the user defined behavioural
parameters. Velocity is added with the current position of the particle to move to another new position in the search
space.



X  X V
(4)
After updating a particle's position, limitations are imposed on the distance covered by the particle in a single step so
that the particle can move from one search space to another in a single step. The steps involved in PSO algorithm are
as follows [13]:
a. Initialize randomly the positions and velocities of each particle.
b. Update the position and velocity of each particle.
c. Update the personal and global best.
d. Find the velocity of a new particle using equation (3).
e. Using equation (4) move the particle to a new position.
f. Enforce search-space boundaries.


f ( X )  f ( P)
g. Update the particle’s best position, if

h. The above steps are repeated for the swarm’s best position (G ) .
The MOL algorithm is similar to PSO algorithm but the difference is that in MOL algorithm the particle is updated
randomly where as in PSO algorithm the particle is updated iteratively over the entire swarm. This simplified version

algorithm the swarm’s best position P is eliminated by
of PSO is also known as Social Only PSO. In the MOL
setting  P =0 and the velocity update formula becomes:




V  w V   G RG (G  X )
(5)
Where w is inertia weight and RG ~ U (0,1) is a stochastic variable weighted by the user defined behavioral


parameter  G . The particles current position is denoted by X and updated using equation (4) as before. G represents
entire swarm's best known position.
5.RESULT AND DISCUSSION
Here the fitness function can be obtained from time-domain simulation of power system model. Using each set of
controller’s parameters, the time-domain simulation is performed and the fitness value is determined. The optimization
was repeated 20 times and the best final solution among the 20 runs is chosen as proposed controller parameters. The
best final solutions obtained in the 20 runs are given in Table I & Table II.
TABLE I. Controller Parameters for SMIB power system with
Signal/
parameters
KS /
K PS
T1S /
T2 S /
T1P
PSS
71.2714
 based
STATCOM
 based PSS
T2 P
T3S /
T3 P
T4 S /
T4 P
1.1820
1.7718
2.3952
1.2649
15.2534
1.9747
0.5917
0.5865
1.1623
PSS
0.7451
2.2308
0.6073
0.3249
0.5634
 based
STATCOM
17.5013
0.7184
2.3188
0.1292
 -based
Pa -based
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TABLE II. Loading condition considered
A.Comparison of
Loading condition
Nominal
P in pu
0.85
δ0 (deg.)
Light
0.5
29.33
Heavy
1
60.3
51.51
 -based STATCOM with Pa -based PSSand with  -based PSS
Case a: nominal loading
The behaviour of the proposed controller is verified at nominal loading condition under severe disturbance condition. A
5 cycle, 3-phase fault is applied at the middle of one transmission line connecting bus 2 and bus 3, at t = 1.0 s. the fault
is removed by opening the faulty line and the lines are reclosed after 5 cycles. The system response under this severe
disturbance is shown in Figs. 4 and 5 where, the response without control is shown with dotted line with legend ‘no
control’, the response with proposed MOL optimized Pa based PSS with  based STATCOM is shown dashed
line with legend case-1 and  based PSS with  based STATCOM is shown with solid line with legend case-2
respectively. It can be seen from Figs. 4 &5 that without control the system is highly oscillatory under the above
contingency. It is also clear from Figs. 4 & 5 that the response with case-2 is better damping characteristics and
enhances greatly the first swing stability compared to case-1.
0.01
 (pu)
0.005
0
-0.005
no control
-0.01
0
case-1
1
2
case-2
3
4
5
Time (sec)
Figure 4.Speed deviation response for nominal load in case-a.
80
70
 (degree)
60
50
40
30
20
no control
10
0
1
case-1
2
case-2
3
4
5
Time (sec)
Figure 5. Power angle responses for nominal loading in case-a
Case b: Light loading
To test the robustness of the proposed controller with different disturbances and operating conditions, the generator
loading changes to light loading as in Table II. A 5 cycle, 3-phase fault is applied at the nearest to bus 3 at t = 1.0 s. the
fault is removed by opening the faulty line and the lines are reclosed after 5 cycles. It can be seen from Fig.6 &7 that
without control the system is highly oscillatory under the above contingency. It is also clear from Fig. 6 & 7 that the
response with case-2 coordinated control of STATCOM is better damping characteristics and enhances greatly the first
swing stability compared to case-1 STATCOM based coordinated controller.
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-3
 (pu)
5
x 10
0
no control
case-1
case-2
-5
0
1
2
3
4
5
Time (sec)
Figure 6 .Speed deviation responses for light loading in case-b
45
40
 (degree)
35
30
25
20
no control
case-1
15
case-2
10
0
1
2
3
4
5
Time (sec)
Figure 7. Power angle responses for light loading in case-b
Case c: Heavy loading
To test the robustness of the controller to operating condition and fault clearing sequence, the generator loading is
changed to heavy loading condition and a 5-cycle, 3-phase fault is applied at Bus2. The fault is cleared by opening both
the lines. The lines are reclosed after 5-cycles and original system is restored. The system response for the above severe
disturbance is shown in Figs. 8 & 9. It can be clearly seen from Figs. 8 &9 that, for the given operating condition and
contingency, the system is unstable without control. Stability of the system is maintained and power system oscillations
are effectively damped out with the application of case-1. The proposed coordinated controller case-2 provides the best
performance and outperforms by minimizing the transient errors and quickly stabilizes the system.
-3
4
x 10
no control
3
case-1
case-2
 (pu)
2
1
0
-1
-2
-3
0
1
2
3
4
5
Time (sec)
Figure 8. Speed deviation responses for light loading in case-c
75
no control
case-1
case-2
 (degree)
70
65
60
55
50
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Time (sec)
Figure 9. Power angle responses for light loading in case-c
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B.Extension to Multi-Machine power system with STATCOM with PSS
From the above analysis comparison of STATCOM with Pa based PSS and  based PSS in SMIB for power
system stability analysis it is clear that coordinated control of  based PSS with  based STATCOM controller
better than coordinated control of Pa based PSS with  based STATCOM controller. Now for the verification
and effectiveness of proposed analysis can be extended to multi machine power system consisting of 3 generators with 5
bus systems is considered. It is similar to the power system used [17, 18]. The proposed multi machine power system
are divided into two subsystem connected by intertie. The improvement of power system stability the line is
sectionalized and a STATCOM is shunted at bus5. The Fig.10 shows the single line diagram of the proposed test
system [9].For remote input signal speed deviation of generator G1 and G3 is chosen as the control input of STATCOM
based damping controller. Speed deviations (  ) and active power ( Pa ) the individual generators are chosen as
the input signals for all three PSSs.
BUS2
BUS4
L2
G2
BUS5
T2
BUS1
L1
L1
LOAD2
G1
L1
L1
T1
BUS3
L3
G3
LOAD1
T3
LOAD4
STATCOM
LOAD3
Figure 10. Three machine power system PSS with STATCOM
TABLE III. Optimized controller Parameters for multi machine power system
Signal/
Parameters
 -based
STATCOM
 -based
PSS1
 -based
PSS2
 -based
PSS3
 -based
STATCOM
P -based
PSS1
P -based
PSS2
P -based
PSS3
KS /
T1S /
T2 S /
T3S /
T4 S /
K PS
T1P
T2 P
T3P
T4 P
98.7935
0.4269
0.6452
0.9926
0.1859
34.2051
1.0066
2.4571
1.0061
1.5521
7.7193
0.9540
0.4037
1.8955
2.1779
17.5395
1.7142
0.7361
1.3270
2.0812
81.4726
2.2646
0.3183
2.2835
1.5813
4.8779
0.6970
1.3677
2.3938
2.4123
7.8815
2.4265
2.3930
1.2140
2.0009
7.0952
1.0550
2.2894
1.9807
2.3988
The objective functions J is defined as
t  t sim
J 
 (  | 
L
|   |   I |)  t  dt
(6)
Where ΔωI and ΔωL are the speed deviations of inter-area and local modes of oscillations respectively and tsim is the
time range of the simulation. The same approach as explained for SMIB case is followed to optimize the STATCOM
Pa based PSS with  based PSS damping controller parameters for three-machine case. The best among the 20
runs for both the input signals are shown in Table III.
t0
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Simulations results
Case-1: Three phase self clearing fault:
Here a 3-phase fault is applied near bus 1 at t = 1 sec and it continues for 5 cycles. In these Figs. 11 &12, the response
without control is shown with dotted line with legend no control; and responses with the signals for Δω based
STATCOM with Pa based PSS is shown with dashed line with legend case-1 and the same for Δω based PSS with
Δω based STATCOM is shown with solid line with legend case-2 respectively. It is clear from Fig. 11 &12 that interarea and local modes of oscillations are highly oscillatory in the absence of STATCOM-based damping controller and
PSS. But the proposed controller significantly improves the power system stability by damping these oscillations with
both case-1 and case-2. However, case-2 based coordinated controller to be a better than case-1 based coordinated
controller as the power system oscillations are quickly damped out with case-1 based coordinated controller.
-3
2-3 (pu)
4
x 10
2
0
no control
-2
case-1
case-2
-4
0
2
4
6
8
10
12
Time (sec)
Figure 11.local mode of oscillation for three phase fault disturbance
x 10
3
-3
no control
1-2 (pu)
2
case-1
case-2
1
0
-1
-2
0
2
4
6
8
10
12
Time (sec)
Figure12. Inter area mode of oscillation for self clearing three phase fault disturbance
Case-2- Line outage disturbance
To show the robustness of the proposed approach, another disturbance is considered. The transmission line between bus
5 and bus 1 is tripped at t=1.0 sec and reclosed after 5 cycles. The system response is shown in Figs.13 & 14 from
which it is clear that case-2 coordinated control to be a better choice than case-1 coordinated control for stability
improvement.
-3
4
x 10
2-3(pu)
2
0
no control
-2
case-1
case-2
-4
0
2
4
6
8
10
12
Time (sec)
Figure 13.Local mode of oscillation for line outage disturbance
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2
x 10
ISSN 2319 - 4847
-3
1-3 (pu)
1
0
-1
no control
case-1
-2
case-2
-3
0
2
4
6
8
10
12
Time (sec)
Figure14.Inter area mode of oscillation for line outage disturbance
Case-3-Small disturbance
For completeness, the load at bus 4 is disconnected for 100 ms and the system response is shown in Figs. 15 &16. It is
clear from these Figs. that the proposed controllers are robust and damps power system oscillations even under small
disturbance conditions. Further, the performance with case-2 coordinated controller to be a better choice than case-1
coordinated controller.
-3
x 10
4
2-3(pu)
2
0
no control
-2
case-1
case-2
-4
0
2
4
6
8
10
12
Time (sec)
Figure 15.Local mode of oscillation for small disturbance
-3
1-2 (pu)
2
x 10
1
0
no control
-1
case-1
case-2
-2
0
2
4
6
8
10
12
Time (sec)
Figure 16. Inter area mode of oscillation for small disturbance
6.CONCLUSION
In this analysis, the proposed MOL optimization technique has been employed for the coordinated design of PSS with
STATCOM based controllers. Two input signal  based
PSS and Pa based PSS are considered. Coordinated design of  based PSS controller with  based
STATCOM controller is compared with coordinated design of Pa based PSS and  based STATCOM controllers
for different loading condition and disturbance. It is observed that  based PSS with  based STATCOM
controller gives better system response than Pa based PSS with  based STATCOM controllers from power
system stability point of view for both SMIB and multi machine power system .
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
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Volume 4, Issue 12, December 2015
ISSN 2319 - 4847
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