Low-Frequency Oscillation Mitigation usin an Optimal Coordination of CES and PSS based on BA

Full text

Low-Frequency Oscillation Mitigation usin an
Optimal Coordination of CES and PSS based on BA
Dwi Lastomo
1
,
Herlambang Setiadi
3
, Galih Bangga
4,5
, Imam Wahyudi Farid
2
, Muhamad Faisal
7
, Peter Go Hutomo
8
,
Taurista Perdana Syawitri
10
, Louis Putra
11
, Yongki Hendranata
12
, Kristiadi Stefanus
13
, Chairunnisa
14
, Andri
Ashfahani
1,2
, and Ahmad Sabila
9
,
1
Department of Automation Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
2
PUI-PT Mechatronics and Industrial Automation, Research Center Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
3
School of Information Technology & Electrical Engineering, The University of Queensland, Brisbane, Australia
4
Mechanical Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
5
Institute of Aerodynamics and Gas Dynamics, University of Stuttgart, Stuttgart, Germany.
6
Department of Cybernetics, Graduate School of Engineering Hiroshima University, Hiroshima, Japan.
7
Non-Installation Top Range Department, Berca Schindler Lifts, Ltd., Indonesia.
8
Toyota SO Department, Astra International Ltd., Indonesia.
9
Civil Engineering Department, Universitas Brawijaya, Malang, Indonesia.
10
Mechanical Engineering Department, Universitas Muhammadiyah Surakarta, Surakarta, Indonesia.
11
Mechanical Engineering Department, Politecnico di Milano, Milan, Italy.
12
Mechanical Engineering Department, Texas A&M University, College Station, USA.
13
Aeronautics Department, Imperial College London, London, United Kingdom.
14
Department of Aircraft Maintenance Engineering, Politeknik Penerbangan Surabaya, Surabaya, Indonesia.
(E-mail: dtomo23@gmail.com, h.setiadi@uq.edu.au, bangga@iag.uni-stuttgart.de, d140188@hiroshima-u.ac.jp,
andriashfahani@gmail.com, Muhamad.faisal@schindler.com, peter.tianxiang@gmail.com, ariqfihris12@gmail.com,
tps123@ums.ac.id, louiszaldhy.purnama@mail.polimi.it, youngkey89@tamu.edu, stefanus.kristiadi16@imperial.ac.uk,
nisachairunnisa61@gmail.com)
Abstract—
Small signal stability represents the reliability of
generator for transferring electrical energy to the
consumers. The stress of the generator increases
proportionally with the increasing number of load demand
as well as the uncertainty characteristic of the load
demand. This condition makes the small signal stability
performance of power system become vulnerable. This
problem can be handled using power system stabilizer
(PSS) which is installed in the excitation system. However,
PSS alone is not enough to deal with the uncertainty of
load issue because PSS supplies only an additional signal
without providing extra active power to the grid. Hence,
utilizing capacitor energy storage (CES) may solve the
load demand and uncertainty issues. This paper proposes a
coordination between CES and PSS to mitigate oscillatory
behavior of the power system. Moreover, bat algorithm is
used as an optimization method for designing the
coordinated controller between CES and PSS. In order to
assess the proposed method, a multi-machine two-area
power system is applied as the test system. Eigenvalue,
damping ratio, and time domain simulations are performed
to examine the significant results of the proposed method.
From the simulation, it is found that the present proposal is
able to mitigate the oscillatory behavior of the power
system by increasing damping performance from 4.9% to
59.9%.
Keywords- BA, CES, damping ratio, eigenvalue, low-frequency
oscillation, PSS, time domain simulation.
I.
I
NTRODUCTION
The demand for electrical energy is increasing significantly
in the last few decades. Increasing demand for electrical
energy has been the main issue for electrical company in order
to provide secure, stable and reliable electricity for the
consumers. The stability and the security of power system can
deteriorate due to perturbation, such as increasing load
demand, uncertainty characteristic of the demand and small
changes of the systems parameter. One of the stability that can
effected by those perturbations is small-disturbance angle
stability.
The ability of power system to maintain the
synchronization after small perturbation is called small-
disturbance angle stability or low-frequency oscillation is the
ability of a power system to maintain the synchronization after
being exposed to a small perturbation [1, 2]. This instability
characteristic is emerging due to the existence of sufficient
damping from the system and can be divided into two
categories. The first one is a local oscillation with the
frequency ranging from 0.7 to 2 Hz. The other one is a global
oscillation (inter-area oscillation) with the frequency ranging
from 0.1 to 0.7 Hz [1-3]. If this stability is not well
maintained, the magnitude of the oscillation may grow larger
Proceeding of EECSI 2018, Malang - Indonesia, 16-18 Oct 2018
978-1-5386-8402-3/18/$31.00 ©2018 IEEE 216

and lead to unstable condition. There are several incident
(fully and partial black out) happened due to low frequency
oscillation problems as reported in [4]. The latest incident
related to low frequency oscillation is happened in Continental
Europe electricity on December 2016 [5]. Generally, this
instability can be overcome by simply adding damper
windings into the rotor of the synchronous generator but the
performance can deteriorate over the time. Another way to
handle this problem is by using power system stabilizer (PSS)
as the additional controller [6]. However, PSS alone is out of
date to overcome this problem as the load demand is increased
over the years and the load demand is tend to have the
uncertainty characteristic. Hence, energy storage devices can
be considered to handle the increasing capacity of load
demand as well as the uncertainty of it.
Energy storage becomes reality in the practical power
system. For example, redox flow batteries, battery energy
storage, superconducting magnetic energy storage, and
capacitor energy storage have been used to solve several
problems in power system [7-13]. Among them, capacitor
energy storage (CES) is becoming popular due to large
capacity and fast response for storing and releasing energy
from the grid. However, designing the controller of CES is
huge efforts especially if CES is installed in a large power
system.
One way out of the above dilemma is by employing an
optimization method based on the nature-inspired algorithm
which has been utilized to solve complex engineering
problems [14-22]. An algorithm such as particle swarm
optimization, genetic algorithm, differential evolution
algorithm, imperialist competitive algorithm, artificial immune
system, firefly algorithm, cuckoo search algorithm and bat
algorithm were demonstrated to perform well in solving
optimization problems [14-22]. In particular bat algorithm
(BA) has shown a good performance finding the optimum
parameter for optimization problems [16].
Hence, the main contribution of this paper is to propose a
method for mitigating the low-frequency oscillation of a
power system using a coordinated control between CES and
PSS. Moreover, to obtain the optimum performance, the
parameters of CES and PSS are optimized by BA.
II. F
UNDAMENTAL
T
HEORY
A. Synchronous generator, excotation, and governor model
For low frequency oscillation study, capturing the dynamic
characteristic of the synchronous generator is essential. As
consequences, transforming the non-linear model of
synchronous machine to linear model and all of the parameters
in direct and quadrature (DQ) axis is required. Fig. 1 shows
the DQ transformation of the synchronous generator.
Furthermore, the mathematical representation of linear model
synchronous generator can be described as (1). The detailed
linear model of the synchronous generator can be found in
[23].
d axis q axis
a axis
b axis c axis
θ
Q
i
Q
i
D
i
F
i
D
i
F
i
b
i
c
i
a
i
s
a
s
c
sb
fb
fc
fa
'n
'n
'n
Fig. 1 DQ transformation of the synchronous generator.
The purpose of excitation system is to set the output of the
generator such as voltage, current, and power factor. These
variables will be adjusted by changing the field in the
generator [23, 24]. To handle a small perturbation such as load
changing, the exciter increases the current injection magnitude
so the terminal voltage will be increased. The purpose of this
process is to handle to the slow response of the governor to
adjust the power input to the generator when the disturbance
occurs. In this study, the excitation system can be represented
as time delay and gain controller as shown in Fig. 2 [23, 24].
ref
V
sT
K
A
A
+1
t
V
fd
E
maxR
V
minR
V
Fig. 2 Block diagram of fast exciter system.
Governor is a controller that serves to set the value of
mechanical torque into the input of the generator. For small
signal stability study, the governor is represented as a constant
gain and first order time delay as illustrated in Fig. 3 [23].
1
1
+Tgs
Kg
d
ω
GSC
m
T
Fig. 3 Block diagram of governor system.
0
00 00
0
000
000
000
00 0
000
0
00 0
00
0000
0
00 0 0 0 00
3333
3
00000
010
kM
rL
Qq
qi
vd
drFi
vF
FrDiD
LkMkMr d
dFD i
vq
qr
QiQ
L i kM i kM i kM i kM i
qdqFqDqQd
Qd
TD
m
ωλ
ω
λ
ωωω
λω
δ
Δ
Δ
Δ
−Δ
Δ
−
−− − Δ
Δ=−
Δ
−− − −
ΔΔ
−
Δ
−


 

 

 

 

 

 

 

 


 


0000
0000
0000
000 00
000 00
00000 0
0000001
LkMkM i
FD
dd
kM L M i
FF R F
kM M L i
DRD D
LkM i
qQq
kM L i
QQ Q
j
τω
δ
Δ
Δ
Δ
Δ
−
Δ
−Δ
Δ



































(1)
Proceeding of EECSI 2018, Malang - Indonesia, 16-18 Oct 2018
217

B. Power system stabilizer
In power system study, PSS is widely used to enhance the
dynamic performance of power systems. PSS is applied as an
excitation system controller to damp the oscillatory condition
of the power system. To produce a damping component, PSS
produces an electric torque component corresponding to the
rotor speed deviation. It receives inputs in the form of a rotor
speed deviation to generate an additional signal to the exciter,
which affects the magnitude of the field voltage and the
magnetic flux on rotor side. It shall be noted that magnetic
flux is directly proportional to the electrical torque generated
on the machine. The electric torque that counters the
mechanical torque of the engine to reduce the oscillation of
frequencies occurring on the machine. Fig. 4 depicts the PSS
model used in this study [25].
sT
sT
w
w
+1
Saturation
maxS
V
minS
V
sT
sT
B
A
+
+
1
1
ω
sT
sT
D
C
+
+
1
1
s
V
PSS
K
Gain Blok Washout Blok lead-lag
Fig. 4 Block diagram of PSS.
C. Dynamic model of CES
A devices that can releasing as well as storing power in
large quantities is CES. The device consist of storage
capacitors and power electronics devices with the associated
controller as well security function. The schematic diagram of
CES is illustrated in Fig. 5 [26, 27].
Fig. 5 Schematic diagram of CES.
Several capacitors connected in parallel and denoted by C
capacitance is the representation of storage capacitor in CES.
Moreover, resistance that denoted by R is also connected in
parallel with the capacitor. 12 pulse back to back converters
are used as an interface between the storage capacitor and grid.
When the converters fails, the current I
d
use bypass thyristor
as a path. Moreover, if the converters fails, the I
d
current will
be diverted to the discharge point of R
D
by the DC-disconnect.
The E
d
voltage can then be described as (2) [26, 27].
0
2cos2
dd dD
E
EIR
α
=−
(2)
To vary the capacitor voltage E
d
, changing of phase angle
is required α. The changes in the direction of current during
charge and discharge are overcome by arranging switches in
reverse using GTO. To operate CES in charging mode, switch
2 and 3 are off, while switch 1 and 4 are on. For discharging
operating condition, the switches are operated in the other way
around. E
d0
can be described in Eq. (3) [26, 27].
221/2
max min
0
[]
2
dd
d
EE
E+
=
(3)
It is worth mentioning that capacitor voltage shall not
exceed the specified upper and lower limits. When the system
is exposed by disturbance, if the capacitor voltage is too low
and if another interference emerges before the voltage returns
to its normal value, the energy will be rapidly drawn from the
capacitor which can lead to a discontinuous condition. To
solve this problem, the lower and upper limit for the voltage of
the capacitor has to be set as presented in (4) and (5) [26, 27].
min 0
30
dd
E
E=
(4)
max 0
1.38
dd
E
E=
(5)
CES
K
DC
sT+1
1
VD
K
−
+
+
+
CES
PΔ
R
sC 1
1
+
d
EΔ
0d
E
dd
EE Δ+
0
d
IΔ
d
IΔ
fΔ
0min
30.0
dd
EE =
0max
38.1
dd
EE =
Fig. 6 Block diagram of CES.
After the voltage reaches the rating value, the voltage is
maintained at this value with a continuous supply of PCS.
Hence, it is sufficient to overcome the resistive drop. Since E
d0
is very small, the firing angle of α is close to 90
0
. The stored
energy is released immediately via power electronics
converters as an AC pulse when there is a sudden increase in
load. After that to regulate the system in the new operating
condition the governor and other control mechanisms start to
work. By absorbing some of the excess energy in the system,
CES can immediately be recharged to full value. After that, the
steady state condition of the system can be achieved [26, 27].
CES can be presented as a second-order differential equation
as shown in Fig. 6 [26, 27]. The block diagram of Fig. 6 can
be described as using (6)-(8) [26, 27].
[. .]
1
CES VD d
di
DC
K
fK E
IsT
Δ− Δ
Δ= +
(6)
1
1
dd
E
I
sC R


Δ= Δ

+

(7)
0
().
CES d d d
P
EEIΔ= +ΔΔ
(8)
III. D
ESIGN
CES
AND
PSS
BASED ON
BA
Bat algorithm is a metaheuristic algorithm inspired by bats
behavior. This algorithm was first established in 2010 by
Yang [28]. Bats use a type of sonar called an echolocation to
detect food, avoid obstacles and search for a nest in the dark.
With the echolocation abilities, bats are able to fly in the night
looking for food without crashing. From this characteristic, the
bat algorithm can be developed with the following rules:
Proceeding of EECSI 2018, Malang - Indonesia, 16-18 Oct 2018
218

• Bats use echolocation to sensor distance and
distinguish between food and obstacles even in the
darkness.
• Bats fly randomly to search for food at a speed v
i
and
at position x
i
with a fixed f
i
frequency, wavelength
variation λ
i
and noise level (A
i
).
• It can be assumed the noise level varies from that the
maximum (A
0
) to minimum constant (A
min
).
It is noted that d
i
is the dimension to find the space or
renewed space. The new solution and speed are indicated by
x
it
and v
it
. The mathematical representation of bat velocity and
position can be described in Eq. (9) [28, 29].
()
min max min
'
1*
1
i
i
ff f f
ttt
vvxxf
iii
ttt
xxv
ii
i
β
=+ −
+
=+ −


+=+
(9)
where β is a random vector taken from a uniform distribution.
Here x* is the optimal location of the whole bat solutions after
comparing all the solutions among all bats on each iteration t.
As the result of the multiplication between λ
i
and f
i
the velocity
of the bats increases. λ
i
or f
i ,
can be used to adjust the velocity
change and enhance the other factors depending on the type of
the problem that will be solved. In the implementation f
min
=0
and f
max
=1 are applied depending on the requirement [28, 29].
Noise level (A
i
) and the pulse emitted from each bat are
always updated according to the iteration process. Noise in
bats decreases when bats have found their prey, while pulse
pants increase. The noise can be selected according to the
exact value, for instance, A
0
=100 and A
min
=1 can be used. To
make it easier, A
0
=1 and A
min
=0 are also employed. Assuming
A
min
=0 means that bats have just found their prey and
temporarily stopped emitting sound. Hence the mathematical
representation can be described in Eq. (10) [28, 29].
()
1,
01exp
tt
AA
ii
t
rr t
ii
α
γ
+=

=−−

(10)
where α and γ are a constant value. For every 0< α <1 and γ>0,
the mathematical representation can be described in Eq. (11)
[28, 29].
0
0, , ~
tt
iii
Arrt→→→
(11)
In the simplest problem, α=γ and α=γ=0.9. In this paper,
BA is used to optimize the parameter of CES and PSS to
mitigate the low-frequency oscillation on the power system.
To find the optimal parameter of CES and PSS,
comprehensive damping index is used as the objective
function of the BA which can be calculated using (12) [30].
()
1
1
n
i
i
CDI
ξ
=
=−
 (12)
IV.
R
ESULTS AND
D
ISCUSSIONS
Investigation of the impact of CES and the proposed
method in low frequency oscillation is reported in this paper.
MATLAB/SIMULINK environment is used as the software
tools to investigate the case study. The test system that use in
this paper is well known two-area power plant. A modification
has been made by adding 100 MW CES in generator 1.
Furthermore, PSS is installed in generator 3. Fig. 5 shows the
schematic diagram of the test system. For identifying the
efficiency of optimal coordination of PSS and CES using BA
in the system, analyzing the eigenvalue and damping
performance of electromechanical (EM) mode are conducted.
Finally, to verify the eigenvalue as well as the damping value
results, comparison of linear time domain analysis is
conducted.
S
S
S
S
15
67
G2 G4
G3
G1
3
10
24
89
11
AREA 1 AREA 2
Fig. 5 Test systems.
A.
Case study 1
The comparison of EM mode under different cases is shown
in Table 1. It is found that the eigenvalue of inter-area and
local mode area-1 are moved towards left-half plane when
CES is added in the generator bus 1. This movement is caused
by decreasing of generator stress due to the additional active
power from CES. It is also monitored that the location of CES
has a significant influence in the system dynamic indicated by
the movement of local mode area 2 that relatively remain in its
position. Furthermore, the proposed method is shown the best
eigenvalue performance compared to any other scenario
indicated by the more negative eigenvalue.
The damping performance of different scenario is illustrated
in Table. 2. It is monitored that by installing PSS in generator-
3, the damping ratio of all EM mode are increased gradually.
This occurs due to additional signal damping from the PSS to
the system. The similar pattern is investigated when CES is
installed in the generator-1 bus. The damping of local mode
area-1 increases significantly from 4.8% to 36%. Furthermore,
the damping of inter-area mode also enhances from 2.6% to
7.7%. However, as mentioned before, that the location of CES
has a significant impact to the system. Hence, the damping of
local mode area-2 is remained in its position.
The damping performances of all EM modes increase
significantly after CES and PSS are installed in the system.
Proceeding of EECSI 2018, Malang - Indonesia, 16-18 Oct 2018
219

Moreover, the proposed method (coordination between CES
and PSS using BA) shows the best performance compared to
the other scenarios indicated by the high value of the damping
performance. It is also found that the oscillation in local mode
area-1 is completely wiped out indicated by the damping value
that becomes 100%. This condition takes place due to an
optimal coordination between CES and PSS based on BA that
can supply large additional signal damping to the system.
Table 1 The comparison of eigenvalue under different cases.
Cases Local mode
1
Local mode
2 Inter-area
Base case -0.324+6.77i -0.342+7.02i -0.071+2.61i
Considering
PSS -0.324+6.76i -0.357+7.03i -0.071+2.61i
Considering
CES -2.67+6.78i -0.34+7.02i -0.21+2.67i
Considering
CES PSS -2.63+6.82i -2.23+9.80i
-0.390+2.96i
Proposed
method -3.7263 -8.26+11i
-0.42+3.41i
Table 2 The comparison of EM mode damping under different cases.
Cases Local mode
1
Local mode
2 Inter-area
Base case 4.8 4.85 2.6
Considering
PSS 4.8 4.88
2.59
Considering
CES 36.65 4.85 7.77
Considering
CES PSS 36 22.17
13.09
Proposed
method 100 59.97
12.25
To verify the eigenvalue analysis, time domain analysis is
carried out. To excite the dynamic response of the system,
small perturbation is made in the system. Figs. 7 shows the
time domain response of rotor speed for generator. Table 3
illustrates the detailed overshoot and settling time comparison
of different scenarios. A blue line represents the oscillatory
condition of base case system (generic two-area power
system) while green line is for the dynamic response of the
rotor speed equipped with PSS. The yellow line represents the
rotor speed dynamic performance due to small perturbation
with CES, while the red line is for the oscillatory condition of
the rotor speed with CES and PSS. Furthermore, a system with
proposed method is presented with black lines. It is monitored
that the best oscillatory condition compared to the others
scenario is a system with the proposed method. This time
domain simulation also verifies the eigenvalue analysis and
damping value analysis by showing that the best performance
compared to other scenarios is obtained when the proposed
method is applied in the system.
0 5 10 15 20 25 3
0
-4
-3
-2
-1
0
1
2
3x 10
-3
t(s)
Amplitudo(pu)
Base case
PSS
CES
CES PSS
CES PSS BA
Fig. 7 The time domain response of rotor speed G1.
Table 3 Detailed time domain response of rotor speed G1.
Cases Overshoot (pu) Settling time (s)
Base case -0.003136 >30
With PSS -0.03134 >30
With CES -0.02283 16.03
CES PSS -0.001981 11.74
Proposed method -0.001658 8.26
B.
Case study 2
In the second case study, comparison of damping
performance of the proposed method with other algorithm. In
this paper imperialist competitive algorithm (ICA) is used to
tune the CES and PSS parameter. Fig 8 illustrates the damping
comparison of EM mode between the proposed method and
CES and PSS based on ICA.
Local 1 Local 2 Inter-area
0
20
40
60
80
100
Proposed method
CES PSS ICA
Fig. 8 Damping comparison.
It is noticeable that the proposed method (using BA) provide
a better damping ratio compared to CES and PSS based on
ICA indicated by higher percentage of the damping.
V.
C
ONCLUSIONS
This paper investigates the significant impact of installing
CES on the small signal stability performance of two-area
power system. A method to mitigate the low-frequency
oscillation by employing coordinated control between CES and
PSS based on BA is proposed. From the simulation results, it is
observed that the damping of the system increases when PSS is
Proceeding of EECSI 2018, Malang - Indonesia, 16-18 Oct 2018
220

utilized in the system. It is monitored that CES enhances the
damping performance of the system by injecting additional
active power to the grid and the location of CES plays an
important role in the system dynamic. By adding PSS and CES
to the system, the stability of the power system increases
significantly indicated by increasing damping performance of
EM mode. The damping performance of EM mode increases
from 4.8% to 100 % for local mode area 1, 4.9% to 59.9 for
local mode area 2, and 2.6% to 12.3% for inter-area. The
reliability of the power system also augments indicated by the
time domain results of generator 1 and 4. Furthermore, the
proposed method is able to damp the low-frequency oscillation
significantly. Further research needs to be conducted by
considering high penetrations of renewable energy sources
(RESs) such as large-scale wind power system or large-scale
PV generation. Installing another PSS such as dual input PSS,
and multi-band PSS may be considered to handle low-
frequency oscillation from different sources. Another
optimization method such as whale algorithm, artificial
immune system clonal selection and firefly algorithm can be
used for designing a coordinated control between PSS and
CES.
R
EFERENCES
[1] H. Setiadi, A. U. Krismanto, and N. Mithulananthan, "Enabling BES in
Large PV Plant for Stability Enhancement on Power Systems with High
RES," in IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia),
2017.
[2] H. Setiadi, A. U. Krismanto, N. Mithulananthan, and M. Hossain, "Modal
interaction of power systems with high penetration of renewable energy
and BES systems," International Journal of Electrical Power & Energy
Systems, vol. 97, pp. 385-395, 2018.
[3] R. S. Wijanarko, H. Setiadi, and T. A. Nugroho, "Coordination of SPS
and CES to Mitigate Oscillatory Condition on Power Systems,"
TELKOMNIKA (Telecommunication Computing Electronics and
Control), vol. 15, no. 4, 2017.
[4] G. Rogers, Power system oscillations. Springer Science & Business
Media, 2012.
[5] entsoe, "Analysis of CE Inter-Area Oscillations of 1st December 2016,"
European Network of Transmission System Operators for Electricity,
2017.
[6] N. Mithulananthan, C. A. Canizares, J. Reeve, and G. J. Rogers,
"Comparison of PSS, SVC, and STATCOM controllers for damping
power system oscillations," IEEE Transactions on Power Systems, vol.
18, no. 2, pp. 786-792, 2003.
[7] D. Lastomo, Atmiasri, and H. Setiadi, "Stability Enhancement of Hybrid
Power System using RFB based on Craziness PSO," in 3nd International
Conference on Science in Information Technology (ICSITech), 2017.
[8] D. Lastomo, H. Setiadi, and M. R. Djalal, "Optimization of SMES and
TCSC using particle swarm optimization for oscillation mitigation in a
multi machines power system," Journal of Mechatronics, Electrical
Power, and Vehicular Technology, vol. 8, no. 1, pp. 11-21, 2017.
[9] D. Lastomo et al., "The effects of energy storages on small signal stability
of a power system," in International Seminar on Intelligent Technology
and Its Applications (ISITIA), 2017.
[10] H. Setiadi, A. U. Krismanto, and N. Mithulananthan, "Influence of BES
system on local and inter-area oscillation of power system with high
penetration of PV plants," in International Conference on Applied System
Innovation (ICASI), 2017.
[11] M. Taufik, D. Lastomo, and H. Setiadi, "Small-disturbance angle stability
enhancement using intelligent redox flow batteries," in 4th International
Conference on Electrical Engineering, Computer Science and Informatics
(EECSI), 2017.
[12] H. Setiadi, N. Mithulananthan, A. U. Krismanto, and R. Shah,
"Comparison of Battery Energy Storage Model for Small Signal Stability
in Power Systems," in IEEE 27th International Symposium on Industrial
Electronics (ISIE), 2018.
[13] M. R. Djalal, H. Setiadi, D. Lastomo, and M. Y. Yunus, "Modal Analysis
and Stability Enhancement of 150 kV Sulselrabar Electrical System using
PSS and RFB based on Cuckoo Search Algorithm," International Journal
on Electrical Engineering and Informatics, vol. 9, no. 4, pp. 800-812,
2017.
[14] M. Abdillah, H. Setiadi, A. B. Reihara, K. Mahmoud, I. W. Farid, and A.
Soeprijanto, "Optimal selection of LQR parameter using AIS for LFC in a
multi-area power system," Journal of Mechatronics, Electrical Power,
and Vehicular Technology, vol. 7, no. 2, pp. 93-104, 2016.
[15] H. Setiadi and K. O. Jones, "Power System Design using Firefly
Algorithm for Dynamic Stability Enhancement," Indonesian Journal of
Electrical Engineering and Computer Science, vol. 1, no. 3, pp. 446-455,
2016.
[16] N. C. Damasceno and O. Gabriel Filho, "PI controller optimization for a
heat exchanger through metaheuristic Bat Algorithm, Particle Swarm
Optimization, Flower Pollination Algorithm and Cuckoo Search
Algorithm," IEEE Latin America Transactions, vol. 15, no. 9, pp. 1801-
1807, 2017.
[17] D. Lastomo, Widodo, H. Setiadi, and M. R. Djalal, "Enabling PID and
SSSC for load frequency control using Particle Swarm Optimization," in
3rd International Conference on Science in Information Technology
(ICSITech), 2017.
[18] H. Setiadi, N. Mithulananthan, and M. J. Hossain, "Impact of battery
energy storage systems on electromechanical oscillations in power
systems," in IEEE Power & Energy Society General Meeting, 2017.
[19] M. Ulum, H. Setiadi, and D. Lastomo, "Design Controller Blade Pitch
Angle Wind Turbine Using Hybrid Differential Evolution Algorithm-
Particle Swarm Optimization," Advanced Science Letters, vol. 23, no. 12,
pp. 12396-12399, 2017.
[20] M. R. Djalal, M. Yunus, A. Imran, and H. Setiadi, "Capacitive Energy
Storage (CES) Optimization For Load Frequency Control in Micro Hydro
Power Plant Using Imperialist Competitive Algorithm (ICA)," EMITTER
International Journal of Engineering Technology, vol. 5, no. 2, pp. 279-
297, 2018.
[21] H. Setiadi, N. Mithulananthan, A. U. Krismanto, and R. Shah, "Low-
Frequency Oscillatory Stability Study on 500 kV Java-Indonesian Electric
Grid," in IEEE 27th International Symposium on Industrial Electronics
(ISIE), 2018.
[22] M. R. Djalal, H. Setiadi, and A. Imran, "Frequency stability improvement
of micro hydro power system using hybrid SMES and CES based on
Cuckoo search algorithm," Journal of Mechatronics, Electrical Power,
and Vehicular Technology, vol. 8, no. 2, pp. 76-84, 2017.
[23] P. M. Anderson and A. A. Fouad, Power system control and stability.
John Wiley & Sons, 2008.
[24] P. Mitra, S. Chowdhury, S. Chowdhury, S. Pal, and P. Crossley,
"Intelligent AVR and PSS with Adaptive hybrid learning algorithm," in
Power and Energy Society General Meeting, 2008.
[25] P. Kundur, N. J. Balu, and M. G. Lauby, Power system stability and
control. McGraw-hill New Y ork, 1994.
[26] V. Mukherjee and S. Ghoshal, "Application of capacitive energy storage
for transient performance improvement of power system," Electric power
systems research, vol. 79, no. 2, pp. 282-294, 2009.
[27] P. Bhatt, R. Roy, and S. Ghoshal, "GA/particle swarm intelligence based
optimization of two specific varieties of controller devices applied to two-
area multi-units automatic generation control," International journal of
electrical power & energy systems, vol. 32, no. 4, pp. 299-310, 2010.
[28] X.-S. Yang, Nature-inspired metaheuristic algorithms. Luniver press,
2010.
[29] D. Sambariya and R. Prasad, "Robust tuning of power system stabilizer
for small signal stability enhancement using metaheuristic bat algorithm,"
International Journal of Electrical Power & Energy Systems, vol. 61, pp.
229-238, 2014.
[30] A. El-Zon koly, A. Khalil, and N. Ahmied, "Optimal tunnin g of lead -lag
and fuzzy logic power system stabilizers using particle swarm
optimization," Expert Systems with Applications, vol. 36, no. 2, pp. 2097-
2106, 2009.
Proceeding of EECSI 2018, Malang - Indonesia, 16-18 Oct 2018
221