Abstract: This paper present the realization and development of a graphical user interface (GUI) for teaching and research in to studied
a genetic algorithm (GA) applied to power system stabilizer (PSS) for adapt a robust H2 controller based an advanced Frequency technique
applied on automatic excitation control of powerful synchronous generators, to improve transient stability and its robustness of a single
machine- infinite bus system (SMIB) .The GA is applied to solve an optimization problem and to achieve control parameters of PSS.
The GUI is a useful average to facilitate stability study of power system with the analysis and synthesis of regulators, and resolution of the
compromise: results precision / calculation speed .The computer simulation results obtained by developed graphical user interface (GUI)
have proved the efficiency and robustness of the robust H2 adapted with a genetic algorithm, in comparison with a conventional PSS,
showing stable system responses almost insensitive to large parameter variations. This robust control possesses the capability to
improve its performance over time by interaction with its environment. The results proved also that good performance and more robust-
ness in face of uncertainties (test of robustness) with the linear robust H2 controller (optimal LQG controller with Kalman Filter) adapted
with a genetic algorithm in comparison with using the classical regulator PID.
Keywords: GUI-MATLAB, Educational Support, powerful synchronous generators, AVR and PSS, LQG control,
Kalman filter, stability and robustness, genetic algorithms.
Received: April 13, 2021. Revised: February 25, 2022. Accepted: March 28, 2022. Published: April 26, 2022.
Power system stabilizers (PSS) have been used for many
years to add damping to electromechanical oscillations. The
use of fast acting high gain AVR’s and the evolution of large
interconnected power systems with transfer of bulk power
across weak transmission links have further aggravated the
problem of low frequency oscillations . The continuous
change in the operating condition and network parameters
result in corresponding changes in the system dynamics [1,
2] . This constantly changing nature of power systems
makes the design of damping controllers a very difficult
task. Power system stabilizers (PSS) were developed to
extend stability limits by modulating the generator excitation
to provide additional damping to the oscillations of syn-
chronous machine rotors. Recent developments in the field
of robust control provide methods for designing fixed para-
meter controllers for systems subject to model uncertainties.
Conventional PSS based on simple design principles such
as PI control and eigenvalue assignment techniques have
been widely used in power systems [3, 5]. Such PSS ensure
optimal performance only at their nominal operating point
and do not guarantee good performance over the entire oper-
ating range of the power system. This is due to external
disturbances such as changes in loading conditions and fluc-
tuations in the mechanical power. In practical power systems
networks, a priori information on these external disturbances
is always in the form of certain frequency band in which
their energy is concentrated.
The stabilizer of this new generation for the system AVR
– PSS, aimed-at improving power system stability, was
developed using the robust controller H2 based on LQG.
This has been advantage of maintaining constant terminal
voltage and frequency irrespective of conditions variations
in the system study. The H2 control design problem is de-
scribed and formulated in standard form with emphasis on
the selection of the weighting function that reflects robust-
ness and performances goals [14]. The proposed system has
the advantages of robustness against model uncertainty and
external disturbances (electrical and mechanical), fast re-
sponse and the ability to reject noise.
The synthesize robust control H2 using an advanced Fre-
quency technique based on power system stabilizer, the parame-
ters tuning of this later is very important for adapt a robust con-
trol H2 with the variations loading conditions, and configu-
rations as the machine parameters change with operating
conditions .The genetic algorithms is a global research
technical and an optimization procedure based on natural
inspired operators such as crossing, and selection
[5,6].unlike other optimization methods, the (G.A) operate
under several encodings parameters (binary, ternary,
real…),to be optimized and not the parameters themselves
.in addition, to better guide the AVR-PSS optimal parame-
ters search ,the (G.A) use a performance index to approach
this solution [6].
GUI (graphical user interface) creates graphical display
in one or more windows containing controls, called compo-
nents that enable a user to perform interactive tasks. The
user of the GUI does not have to create a script or type
commands at the command line to accomplish the tasks [18,
19]. Unlike coding programs to accomplish tasks, the user of
a GUI need not understand the details of how the tasks are
Educational Support Using Graphical User Interfaces to a
Robust Stabilizer H2-PSS Besed Genetic Algorithms study
GHOURAF DJAMEL EDDINE1 , SAYEH ABDELKADER1 , NACERI ABDELLATIF2
1SCAMRE Laboratory, National Polytechnic School , Department of Electrical Engineering, BP1523 EL
M’naour, Oran 31000, ALGERIA
2Department of Electrical Engineering, DL University of SBA IRECOM Laboratory, BP 98 22000 ALGERIA
1. Introduction
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DOI: 10.37394/23209.2022.19.9
Ghouraf Djamel Eddine,
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E-ISSN: 2224-3402
89
Volume 19, 2022
performed. The graphical user interface (GUI) can make the
understanding of the effects of stability study of power sys-
tem with the analysis and synthesis of regulators, and resolu-
tion of the compromise: results precision / calculation speed.
Simulation results obtained by developed graphical user
interface (GUI) shown the evaluation of the proposed linear
control methods based on this advanced frequency tech-
niques optimized by the genetic algorithms applied in the
automatic excitation regulator of powerful synchronous
generators: the robust H2 linear stabilizer and conventional
PID control optimized by the genetic algorithms schemes
against system variation in the SMIB power system, with a
test of robustness against parametric uncertainties of the
synchronous machines (electric and mechanic), and make a
comparative study between these two control techniques for
AVR – PSS systems.
In this paper the dynamic model of an IEEE - standard of
power system, namely, a single machine connected to an
infinite bus system (SMIB) was considered [4]. It consists of
a single synchronous generator (turbo-Alternator) connected
through a parallel transmission line to a very large network
approximated by an infinite bus as shown in figure 1.
Figure 1 Standard system IEEE type SMIB with excitation control of
powerful synchronous generators
This paper is based on the Park modeling of powerful
synchronous generators. The PSG model is defined by equa-
tions (1to 12) [4, 15]:
[ ]
[ ]
[ ]
[ ]
fd
A
trefA
A
fd
qqqd
q
d
fddddq
d
q
emmom
m
momB
E
T
VVK
Tdt
dE
ixxE
T
dt
dE
EixxE
T
dt
dE
TTSSD
Hdt
d
SS
dt
d
1
)(
1
)(
1
)(
1
)(
2
1
)(
''
'
0
'
''
'
0
'
'
−−=
−+−=
+−+−=
−+−−=
−=
δ
ω
δ
The first two equations are obtained from the second order
swing equation as
B
em
H
MTT
dt
d
D
dt
d
M
ω
δδ
2
with
'
2
2
=−=+
The stator equations in dq & DQ reference frames are given
as
))(()(
))(()(
If
'''
'''
'''
''
''
jxRjiijEEjVV
jxRjiijEEjVV
xxx
iRixEV
iRixEV
aDQDQDQ
adqdqdq
dq
daqqdd
qaddqq
++−+=+
++−+=+
==
−−=
−+=
The system equations such as the torque, state variable equa-
tions are also presented here in this context. The electrical
torque, Te is expressed in terms of state variables E’d and E’q
and the non-state variables id and iq and is expressed as
qdqdqqdde
iixxiEiET )(
''''
−++=
The direct axis and quadrature axis currents in the synchron-
ous machine, id and iq can be obtained from the following
linear equations as
++
++
=
−−
−+
q
d
IdRa
RaId
dbb
qbb
i
i
zxzR
zRzx
EEhEh
EEhEh
)( )(-
)(- )(
)cos()cos(
)cos()cos(
'
'
'
12
'
21
δδ
δδ
Here, in the above equation, (zR + j zI) is the input impedance
of the external network viewed from the generator terminals
with infinite bus shorted. (h1 + jh2) is the voltage gain at the
terminals with armature open circuited. The initial condi-
tions for the system equations for Pto, Qto, Vto and θ0are ob-
tained from the power flow analysis in steady state as
000
''
0
'
00
'
00
0
'
0
0
'
00
000
0000
0000
0000
0000
000000
00
0
00
)(
)(
)(
)(
)cos(
)sin(
)cos(
)sin(
)(
mqdqdddqqe
qqqq
qddfdq
dqdqfd
tq
td
aq
ad
aqatq
t
t
a
TiixxiEiET
ixxd
ixxEE
ixxEE
Vv
Vv
Ii
Ii
IjxRVE
V
jQP
I
=−++=
−−=
−−=
−−=
−−=
−−=
−=
−−=
∠++∠=∠
−∠
−
=∠
θδ
θδ
φδ
φδ
φθδ
θ
φ
The AVR (Automatic Voltage Regulator), is a controller
of the PSG voltage that acts to control this voltage, thought
the exciter .Furthermore, the PSS was developed to absorb
the generator output voltage oscillations [1].
In our study the synchronous machine is equipped
by a voltage regulator model "IEEE" type – 5 [7, 8], as is
shown in Figure 4.
Figure 2 . A simplified” IEEE type-5” AVR
FrefE
A
REA
RVVV
T
VVK
V−=
−
= ,
(11)
(1)
(23)
(2)
(3)
(4)
(5)
(6)
(10)
(9)
(8)
(7)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
SG
Infinity bus
Exciter
Power System
Stabilizer PSS
Exciter System
Stabilizer
∆ω
+
+
+
∆If
Automatic Voltage Regula-
tor AVR
Ug
U ref
2. System Description Studied
Under GUI
2.1. Dynamic Power System Model
2.1.1. Power System Description
2.1.2. The modeling of powerful
synchronous generators
2.1.3. Models of regulators AVR and PSS:
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In the PSS, considerable’s efforts were expended for the
development of the system. The main function of a PSS is to
modulate the SG excitation to [1, 2, and 4].
Figure 3. A functional diagram of the PSS used [8]
In this paper the PSS signal used, is given by: [9]
input
K
VV
T
V
V
V
T
T
T
VV
V
V
T
T
T
VV
V
PSS
W
∆
==
+
−
=
+
−
=
.
.
1
.
1
3
.
3
.
2
2
3
2
23
.
2
.
2
1
2
1
12
.
1
;
;
;
The control system design method by means of modern
FSM algorithms is supposed to have some linear test regula-
tor. It is possible to collect various optimal adjustment of
such a regulator in different operating conditions into some
database. Linear – Quadratic – Gaussian (LQG) control
technique is equivalent to the robust H2 regulator by mi-
nimizing the quadratic norm of the integral of quality [13].
In this work, the robust quadratic H2 controller (corrector
LQG) was used as a test system, which enables to trade
off regulation performance and control effort and to take into
account process and measurement noise [11,5]. LQG
design requires a state-space model of the plant:
+=
+=
DuCxy
BuAx
dt
dx
Where x, u, y is the vectors of state variables, control in-
puts and measurements, respectively.
Figure 7. Optimal LQG regulated system with Kalman filter.
The goal is to regulate the output y around zero. The
plant is driven by the process noise w and the controls u, and
the regulator relies on the noisy measurements yv = y+v to
generate these controls. The plant state and measurement
equations are of the form:
Both w and v are modeled as white noise.
In LQG control, the regulation performance is meas-
ured by a quadratic performance criterion of the form:
The weighting matrices Q, N and R are user specified
and define the trade-off between regulation performance and
control effort.
The LQ-optimal state feedback u= –kx is not imple-
mented without full state measurement. However, a state
estimate x
ˆ can be derived such that u = −kx
ˆ remains
optimal for the output-feedback problem.
This state estimate is generated by the Kalman filter:
Thus, the LQG regulator consists of an optimal state-
feedback gain and a Kalman state estimator (filter),
as shown in figure 7.
On the basis of investigation carried out, the main
points of fuzzy PSS automated design method were formu-
lated [6]. The nonlinear model of power system can be
represented by the set of different linearized model shown
in equations (29). For such model, the linear compensator in
the form of u = –Kx can be calculated by means of LQG
method. The family of test regulators is transformed into
united fuzzy knowledge base with the help of hybrid learn-
ing procedure (based variable structure sliding
mode). In order to solve the main problem of the rule base
design, which is calle “the curse of dimensionality”, and
decrease the rule base size, the scatter partition method
[13] is used. In this case, every rule from the knowledge
base is associated with some optimal gain set. The advan-
tage of this method is practically unlimited expansion of
rule base. It can be probably needed for some new operat-
ing conditions, which are not provided during learning
process. Finally, the robust H2 stabilizer was obtained by
minimizing the quadratic norm
2
2
M of the integral of
quality J(u) in (28), where
[
]
., )()(
2/12/1
0
ω
jsRuQxZandxsMsZ
TT
===
[6].
The basic structure of the control system of a powerful
synchronous generator with the robust controller is shown
in Figure 8.As command object, we have synchronous ge-
nerator with regulator AVR-FA (PID with conventional
PSS), an excitation system (exciter), an information block
and measures (BIM) of output parameters to regulate.
Figure 8. Structure of the power
system with robust H
2
controller [3]
PSS
K
W
W
pT
pT
+1
2
1
1
1
pT
pT
+
+
V1
V2
V3
V
PSSmax
V
PSSma
V
PSS
∆ input
−=
−=
−=
=
∆
∆
∆
∫
∆
∆
UUU
and
III
and
or
p
P
input
fff
fff
mach
0
0
0
,
ωωω
(24)
Kalman
Filter
LQG regulator
(25)
dtNuxRuuQxxuJ
TTT
∫
∞
++=
0
)2()(
)
ˆ
(
ˆ
ˆDuxCyLBuxA
dt
xd
v−−++=
(29)
(28)
(26)
(27)
+=
++=
)()()()(
)()()()()()(
twtxtCty
tvtutBtxtAtx
v
&
AVR
PSS
Robust Controller
H2
Exciter
BIM
SG
U
ref
∆f, f’, ∆u, u’, ∆if, if’, ∆P
UG
2.2. A Robust H2-PSS Design
Based on LQG Control and Kalman Filter
2.3. Structure of the power System
with Robust H2 Controller
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Overall, a Genetic Algorithm handles the potential solutions
of a given problem, to achieve the optimum solution, or a
solution considered as satisfactory .the algorithm is orga-
nized into several steps and works iteratively. The figure 7
shows the most simple genetic algorithm introduced by
Holland [14].
Figure 9. The genetic algorithm organization
In what follows, we will describe in more detail the vari-
ous steps of a simple genetic algorithm Figure 9
Coding and initialization: The first step is the problem
parameters coding in order to constitute the chromosomes.
The most used type of coding is the binary one, but other
coding can be also used for example: ternary, integer,
real…the passage from the actual representation to the
coded one is done through encoding and decoding functions.
Selection: A new set of individuals is former with clone’s mul-
tiplicity proportional to the respective ratings of the individuals
of the previous generation: the higher the rating of a given
individual, the more the number of its clones. A popular method
is known as roulette wheel, where individuals are selected
based on their ratings.
Crossover: Part of the new set is combined randomly in pairs
by exchanging random segments of genes.
Mutation: Part of the new set has some of their individuals
mutated, that is, random genes of random individuals are
changed with a given probability.
Population replacement: The original generation is replaced
by the new one, or a new one is created when there is no pre-
vious one.
2
The adaptation of the robust control H2 based on the op-
timization of the regulation system AVR-PSS this latter
considered as control object (figure 8) to synthesize a robust
stabilizer PSS-H2
The synthesis of a robust stabilizing H2 PSS-adapted by
genetic algorithms passes in two steps:
First step: optimization of the control system AVR-PSS by
genetic algorithms.
The purpose of the PSS use is to ensure satisfactory os-
cillations damping, and ensure the overall system stability to
different operation points. To meet this goal, we using a
function composed of two multi-objective functions [16].
This function must maximize the stability margin by increas-
ing damping factors while minimizing the system real ei-
genvalues .
)min()max(
ζσ
+−=
obj
F
The multi-objective function calculating steps are:
1-formulate the linear system in an open –loop (without
PSS);
2-locate the PSS and its parameters initialized by the G.A
through an initial population;
3- Calculate the closed loop system eigenvalues and take
only the dominant modes:
ω
σ
λ
j
±
=
4- Find the system eigenvalues real parts (σ) and damping
factor ζ;
5- Determine the (
ζ
) minimum value and the (- σ) maxi-
mum value, which can be formulated respectively as: (min-
imum (
ζ
)) and (maximum ˗ (σ));
6- Gather both objective functions in a multi-objective func-
tion F as follows:
)min()max(
ζσ
+−=
obj
F
7- Return this Multi-objective function value the to the AG
program to restart a new generation.
Second step: synthesis of robust controller PSS-H2 based to
AVR-PSS optimized
2. GLOVER - DOYLE Algorithm to Synthesize a Robust
Stabilizer PSS- H2
The standard problem in figure (5) consists in finding a
controller K (s) stabilizing internally P (s) and minimizing
the norm H ∞ of the transfer matrix in closed loop of w to z.
Problem solving of standard control is proposed as fol-
lows [17]:
1. Calculates the Standing regime established (RP);
2. Linearization of the control object (SG+PSS+AVR)
3. The main problem in H2 control and the definition of the
control object increased P(s) in the state space:
3-1. Choice of weighting functions: W1, W2, W3
3-2. The obtaining of the command object increased
from weighting functions W1,2,3 .
4. Verify if all conditions to the ranks of matrices are satis-
fied, if not we change the structure of the weighting func-
tions;
5. Choosing a value of γ (optimization level);
(30)
START
Specify
the
parameters
for GA
Generate the initial population
Find the fitness of each particle in the
current population
Gen .>Max .gen ?
Stop
Apply GA operators :
Selection ,Grossver ,Mutation
Gen.=Gen.+1
Time
-
domain simulation
Yes
No
Gen.=1
2.4. The Genetic Algorithms Theory
2.4.1. Introduction
2.4.2. The Genetic Algorithm Steps Description [14]
2.3. Application of the Algorithm
Genetic to adapt a robust control H2
2.3.1. Objective function
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6. Solving two Riccati equations which defined by the two
matrices H and J of HAMILTHON;
7. Reduction of the regulator order if necessary
8. By obtaining optimum values and two solutions of Riccati
equations we get the structure of controller H2 and the roots
of the closed loop with the robust controller;
9. We get the parameters of robust controller H2 in linear
form 'LTI (SS state space, TF transfer function or ZPK zeros
- pole - gains)
10. The simulation and realization of the stability study and
robustness of power system under different functioning
conditions.
Figure 13 shows the proposed in this paper the GA for the
PSS parameters optimization for adapt a robust controller
Figure 13. The multi-objective function and GA algorithm for robust
optimization of AVR-PSS for adapt a robust controller H
2
GUIDE, the MATLAB graphical user interface devel-
opment environment, provides a set of tools for creating
graphical user interfaces (GUIs). These tools greatly simpli-
fy the process of designing and building GUIs. You can use
the GUIDE tools to perform the following tasks [20]:
1. Lay out the GUI.
Using the GUIDE Layout Editor, you can lay out a GUI
easily by clicking and dragging GUI components—such as
panels, buttons, text fields, sliders, menus, and so on—into
the layout area. GUIDE stores the GUI layout in a FIG-file.
2. Program the GUI.
“GUIDE automatically generates a MATLAB program
file that controls how the GUI operates. The code in that file
initializes the GUI and includes function templates for the
most commonly used callbacks for each component—the
commands that execute when a user clicks a GUI compo-
nent. Using the MATLAB editor, you can add code to the
callbacks to perform the functions you want.
3. Starting GUIDE
Start GUIDE by typing guide at the MATLAB command
prompt. This command displays the GUIDE Quick Start
dialog box, as shown in the following figure 5.
Figure 5.
The GUIDE Quick Start dialog box
From the GUIDE Quick Start dialog box, you can per-
form the following tasks:
• Create a new GUI from one of the GUIDE templates pre-
built GUIs that you can modify for your own purposes.
• Open an existing GUI.
4. The Layout Editor
When we open a GUI in GUIDE, it is displayed in the
Layout Editor, which is the control panel for all of the
GUIDE tools. The following figure shows the Layout Editor
with a blank GUI template.
Figure 6.
The Layout Editor
3. The Developed GUIs Under MATLAB
3.1. Introduction
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We consider the simple case of function with tow variables
“X1, X2” belonging to the natural numbers set:
3
2
3
313 Subject to
)X2-)exp(-X1X2-X1-(X1-)1)+(X2-exp(-X1X1)-(1)2,1(
Minimise
22532.22
−
≥
>
−≥>
=
X
X
XXF
obj
The used parameters:
• A 8 bits binary encoding ;
• The search interval :X1 ϵ [-3,3], X2 ϵ [-3,3], ;
• Tournement Method;
• A simple crossing (to one point),with crossing prob-
ability Pc=0.7 ;
• A mutation probability Pm=0.3
To run and view the various steps of genetic algorithm, we
created and developed a “GUI” (Graphical User Interfaces)
in MATLAB software, this latter allows:
• To calculate and display the AG operations
(Coding and initialization, Evaluation, Selection,
Crossover and mutation);
• To display graphically the problem solution, as is
shown in figures 10 and 11.
Figure 10. The genetic algorithm operting developped under GUI /
MATLAB
Figure 11. Optimization result by AG
The “SMIB” system used in our study includes:
• A powerful synchronous generator (PSG) ;
• Two voltage regulators: AVR and AVR-PSS con-
nected to;
• A Power Infinite network line
The SMIB mathematical model based on Park model is
used for simulation in this paper and is shown in Figure 14.
For deferent block detail see Appendix (2, 3 and 4)
Figure 14. Structure of the synchronous generator (PARK model) with
the excitation controller under [10].
The optimized parameters for PSS are: K0w, K1w, T1w, and
T0w, and the PSS-AVR model used shows in figure 13 with:
1.00001.0
1.00005.0
100
100
2
1
1
0
≤≤
≤≤
≤≤
≤
≤
T
T
K
K
w
w
Figure 444 The used model of the AVR-PSS
To analyzed and visualized the different dynamic beha-
viors we have creating and developing a “GUI” (Graphical
User Interfaces) under MATLAB .This GUI allows as to:
• Perform control system from PSS controller;
• To optimized the controller parameters by Genetic
Algorithm;
• View the system regulation results and simulation
(see GUI-MATLAB in the Appendix 3 created);
• Calculate the system dynamic parameters ;
• Test the system stability and robustness;
0.3Pmy probabilitmutation A
0.7Pcy probabilit crossingA
201 Generation Maximum
100 sIndividual ofNumber
=
=
=
=
-4 -2 02
4
-4
-2
0
2
4
-1
0
1
2
X1
X2
fitness
0 5 10 15 20 25
-0.52
-0.5
-0.48
-0.46
-0.44
-0.42
-0.4
Nombre de génération
-4
-2
0
2
4
-4
-2
0
2
4
-1
-0.5
0
0.5
1
1.5
2
X1
X2
fitness
Resu-opt:X1=0.52941 ,X2=-1.6118,f(X1,X2)=-0.40829
fitness
3.2. Creation of a GUI to the
Genetic Algorithms Theory Study
3.3. Creation of a calculating
code under MATLAB / SIMULINK
3.4. A developed GUI/MATLAB for
PSS optimization using GA
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• Study the different
operating regime
excited, rated and over excited
regime)
The different operations are performed
lized under MATLAB and shown in figure
12.
Figure 12 The realised GUI / MATLAB
We present an example for
optimization and tuning the
parameters of the GA-
PSS using our realized GUI, with:
Number of individuals=10 , Number of population =10
********* Creation of initial population ********
********* 1st step coding and initialization ********
=====================================================================================
N ind K0W K1W T1 T2
Sigma ksi
__________________________________________________________________________________________
Indi:01 +01.2471 +00.8431 0.0828 0.0828
-
-----------------------------------------------------------------------------------------------------------------------------
Indi:02 +01.4824 +00.3765 0.0590 0.0600
-
-----------------------------------------------------------------------------------------------------------------------------
Indi:03 +00.5647 +00.2706 0.0145 0.0816
-
-----------------------------------------------------------------------------------------------------------------------------
Indi:04 +04.2353 +00.1216 0.0594 0.0953
-
-----------------------------------------------------------------------------------------------------------------------------
Indi:05 +05.1294 +00.6902 0.0727 0.0647
-
-------------------------------------------------------------------------------------------------
-------------------------------------------------
Indi:06 +04.7294 +00.5529 0.0430 0.0628
-
--------------------------------------------------------------
------------------------------------------------------------------------------------
Indi:07 +04.4471 +00.0078 0.0212 0.0643
-
----------------------------
----------------------------------------------------------------------------------------------------------------------
Indi:08 +00.6118 +00.4157 0.0266 0.0060
-
-----------------------------------------------------------------------------------------------------------------------------
Indi:09 +03.1059 +00.4353 0.0992 0.0937
-
-----------------------------------------------------------------------------------------------------------------------------
Indi:10 +05.0588 +00.7843 0.0481 0.0898
-
-----------------------------------------------------------------------------------------------------------------------------
********* 2sd step selection ********
======================================================================================
N ind K0W K1W T1 T2 Sigma
______________
____________________________________________________________________________
Indi:01 +01.4824 +00.3765 0.0590 0.0600
-
-------------------------------------
-------------------------------------------------------------------------------------------------------------
Indi:02 +01.4824 +00.3765 0.0590 0.0600
-
---
-----------------------------------------------------------------------------------------------------------------------------
Indi:03 +04.2353 +00.1216 0.0594 0.0953
-
-----------------------------------------------------------------------------------------------------------------------------
Indi:04 +04.7294 +00.5529 0.0430 0.0628
-
-----------------------------------------------------------------------------------------------------------------------------
Indi:05 +04.7294 +00.5529 0.0430 0.0628
-
-----------------------------------------------------------------------------------------------------------------------------
Indi:06 +04.7294 +00.5529 0.0430 0.0628
-
-----------------------------------------------------------------------------------------------------------------------------
Indi:07 +04.4471 +00.0078 0.0212 0.0643
-
-------------------------------------------------------------------------------------------
-------------------------------------------------------
Indi:08 +03.1059 +00.4353 0.0992 0.0937
-
---------------------------------------------------------
-----------------------------------------------------------------------------------------
Indi:09 +05.0588 +00.7843 0.0481 0.0898
-
-----------------------
---------------------------------------------------------------------------------------------------------------------------
Indi:10 +04.7294 +00.5529 0.0430 0.0628
-
-----------------------------------------------------------------------------------------------------------------------------
********* 3th step crossover********
==========================================================
============================
N ind K0W K1W T1 T2 Sigma
__________________________________________________________________________________
Indi:01 +01.4824 +00.3765 0.0590 0.0600 -
00.5294 +0.0538 0.695
Pc < PC: There is a crossover 00111111011000001001011010011001
--------------------------------------
------------------------------------------------------------------------------------------------------------
Indi:02 +01.4824 +00.3765 0.0590 0.0600 -
00.5294 +0.0538 0.695
Pc < PC: There is a crossover 00111111011000001001011010011001
-----------------------------------------------------------------------------------------------------------------------------
Indi:03 +03.2000 +00.0588 0.0688 0.0890 -
00.5294 +0.0538 0.310
Pc < PC: There is a crossover 1000100000001111101011111110 0011
----------------------------------------------------------------------------------------------------------------
Indi:04 +05.7647 +00.6157 0.0337 0.0691 -
00.5294 +0.0538 0.310
Pc < PC: There is a crossover 11110101100111010101010110110000
---------------------
-----------------------------------------------------------------------------------------------------------------------------
Indi:05 +04.7294 +00.5529 0.0430 0.0628 -
02.4673 +0.2311
Pc < PC: There is a crossover 11001001100011010110110110100000
-----------------------------------------------------------------------------------------------------------------------------
Indi:06 +04.7294 +00.5529 0.0430 0.0628 -
02.4673 +0.2311 0.344
Pc < PC: There is a crossover 11001001100011010110110110100000
--------------------------------------------------------------------------------------------
------------------------------------------------------
Indi:07 +03.1294 +00.1804 0.0243 0.0675 -
01.5648 +0.1580 0.076
Pc < PC: There is a crossover 10000101001011100011110110101100
operating regime
(under-
regime)
.
from GUI rea-
12.
optimization and tuning the
PSS using our realized GUI, with:
Number of individuals=10 , Number of population =10
=====================================================================================
Sigma ksi
__________________________________________________________________________________________
-
0.2217 +0.0225
-----------------------------------------------------------------------------------------------------------------------------
---------------------
-
0.5294 +0.0538
-----------------------------------------------------------------------------------------------------------------------------
---------------------
-
0.1603 +0.0168
-----------------------------------------------------------------------------------------------------------------------------
---------------------
-
0.5294 +0.0538
-----------------------------------------------------------------------------------------------------------------------------
---------------------
-
0.5294 +0.0538
-------------------------------------------------
-
2.4673 +0.2311
------------------------------------------------------------------------------------
-
2.4172 +0.2425
----------------------------------------------------------------------------------------------------------------------
-
0.2397 +0.0255
-----------------------------------------------------------------------------------------------------------------------------
---------------------
-
0.4707 +0.0453
-----------------------------------------------------------------------------------------------------------------------------
---------------------
-
1.8467 +0.1662
-----------------------------------------------------------------------------------------------------------------------------
---------------------
======================================================================================
N ind K0W K1W T1 T2 Sigma
ksi
____________________________________________________________________________
-
00.5294 +0.0538
-------------------------------------------------------------------------------------------------------------
-
00.5294 +0.0538
-----------------------------------------------------------------------------------------------------------------------------
------------------
-
00.5294 +0.0538
-----------------------------------------------------------------------------------------------------------------------------
---------------------
-
02.4673 +0.2311
-----------------------------------------------------------------------------------------------------------------------------
---------------------
-
02.4673 +0.2311
-----------------------------------------------------------------------------------------------------------------------------
---------------------
-
02.4673 +0.2311
-----------------------------------------------------------------------------------------------------------------------------
---------------------
-
02.4172 +0.2425
-------------------------------------------------------
-
00.4707 +0.0453
-----------------------------------------------------------------------------------------
-
01.8467 +0.1662
---------------------------------------------------------------------------------------------------------------------------
-
02.4673 +0.2311
-----------------------------------------------------------------------------------------------------------------------------
---------------------
============================
N ind K0W K1W T1 T2 Sigma
ksi Pc
__________________________________________________________________________________
_______________
00.5294 +0.0538 0.695
------------------------------------------------------------------------------------------------------------
00.5294 +0.0538 0.695
-----------------------------------------------------------------------------------------------------------------------------
---------------------
00.5294 +0.0538 0.310
----------------------------------------------------------------------------------------------------------------
----------------------------------
00.5294 +0.0538 0.310
-----------------------------------------------------------------------------------------------------------------------------
02.4673 +0.2311
0.344
-----------------------------------------------------------------------------------------------------------------------------
---------------------
02.4673 +0.2311 0.344
------------------------------------------------------
01.5648 +0.1580 0.076
-----------------------------------------------------------------------------------------------------------------------------
Indi:08 +04.4235 +00.2627 0.0961 0.0906
Pc < PC: There is a crossover 10111100010000111111010111100111
-----------------------------------------------------------------------------------------------------------------------------
Indi:09
+04.7765 +00.8000 0.0434 0.0882
Pc < PC: There is a crossover 11001011110011000110111011100001
------------------------------------------------------------------------
--------------------------------------------------------------------------
Indi:10 +05.0118 +00.5373 0.0477 0.0643
Pc < PC: There is a crossover 110101011000
10010111100110100100
-----------------------------------------------------------------------------------------------------------------------------
******** 4th step
Mutation
====================================================
N ind K0W K1W T1 T2 Sigma
____________________________________________________________________________
Indi:01 +02.6118 +00.4235 0.0579 0.0538
0 1 1 0 1 1 1 1 0 1 1 0 1 1 0 0 1 0 0 1 0 0 1 1 1 0 0 0 1 0 0 1
----------------------------
----------------------------------------------------------------------------------------------------------------------
Indi:02 +01.2941 +00.9412 0.0313 0.0361
0 0 1 1 0 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 0 1 0 1 1 1 0 0
-----------------------------------------------------------------------------------------------------------------------------
Indi:03 +00.6118
+00.3059 0.0028 0.0882
0 0 0 1 1 0 1 0 0 1 0 0 1 1 1 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 1
--------------------------------------------------------------------------------------------
Indi:04 +02.7529 +00.5843 0.0454 0.0600
0 1 1 1 0 1 0 1 1 0 0 1 0 1 0 1 0 1 1 1 0 0 1 1 1 0 0 1 1 0 0 1
-----------------------------------------------------------------------------------------------------------------------------
Indi:05 +05.2471 +00.3882 0.0992 0.0961
1 1 0 1 1 1 1 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1
-----------------------------------------------------------------------------------------------------------------------------
Indi:06
+04.7294 +00.5529 0.0938 0.0628
1 1 0 0 1 0 0 1 1 0 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0
----------------------------------------------------------
----------------------------------------------------------------------------------------
Indi:07 +03.1294 +00.6353 0.0489 0.0064
1 0 0 0 0 1 0 1 1 0 1 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 0 0
-----------------------------------------------------------------------------------------------------------------------------
Indi:08 +05.2706 +00.2627
0.0727 0.0616
1 1 1 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1 0 0 1 1 0 0 1 1 1 0 1
----------------------------------------------------------------------------------------------------------------------
Indi:09 +03.6941 +00.8000 0.0426 0.0248
1 0 0 1 1 1 0 1 1 1 0 0 1 1 0 0 0 1 1 0 1 1 0 0 0 0 1 1 1 1 1 1
--------------------
-----------------------------------------------------------------------------------------------------------------------------
Indi:10 +02.0000 +00.5137 0.0481 0.0424
0 1 0 1 0 1 0 1 1 0 0 0 0 0 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 0 0
-----------------------------------------------------------------------------------------------------------------------------
*********
Optimization results
======================================================================================
N Pop K0W K1W T1 T2 Sigma ksi
________________
__________________________________________________________________________
Pop:01 +05.8588 +00.5059 +00.0555 0.0075
---------------------------------------------------
-----------------------------------------------------------------------------------------------
Pop:02 +04.2588 +00.1804 +00.0325 0.0365
------------------------------
--------------------------------------------------------------------------------------------------------------------
Pop:03 +04.6824 +00.4510 +00.0372 0.0448
--------
-----------------------------------------------------------------------------------------------------------------------------
Pop:04 +04.4000 +00.1608 +00.0052 0.0534
-----------------------------------------------------------------------------------------------------------------------------
Pop:05 +05.6235 +00.6863 +00.0590 0.0307
-----------------------------------------------------------------------------------------------------------------------------
Pop:06 +06.0000 +00.3216 +00.0516
0.0150
-----------------------------------------------------------------------------------------------------------------------------
Pop:07 +05.9294 +00.3451
+00.0887 0.0115
-----------------------------------------------------------------------------------------------------------------------------
Pop:08 +05.5529
+00.3137 +00.0637 0.0475
-----------------------------------------------------------------------------------------------------------------------------
Pop:09
+05.2706 +00.4392 +00.0263 0.0444
-----------------------------------------------------------------------------------------------------------------------------
Pop:10 +05.3176 +00.4392 +00.0579 0.0444
-------------------------------------------------------------------------------------------------------------------
Optimization is finished.......
The obtained optimizing parameters are:
+00.3216 T1=+00.0516 T2= 0.0150 with Sigma=
Table 1 give a simulation result optimized PSS param
ters with different SG
TABLE I.
T
HE
P
SS
O
PTIMIZED
parameters TBB-200 TBB-500
T1 0.0321 0.029
T2 0.054 0.0322
K0W 4.074 5.011
K1w 5.43 6.45
st AG
To analyzed and visualized
the
different dynamic beh
viors
, we have created and devel
oped
User Interfaces) under
MATLAB. T
his GUI allows as to:
• Perform controls system
H2-PSS - GA;
• View the system regu
latio
n results and
• Calculate the
system dyna
mic parameters
• Test
the system stability a
nd
-----------------------------------------------------------------------------------------------------------------------------
---------------------
Indi:08 +04.4235 +00.2627 0.0961 0.0906
-00.7595 +0.0702 0.076
Pc < PC: There is a crossover 10111100010000111111010111100111
-----------------------------------------------------------------------------------------------------------------------------
---------------------
+04.7765 +00.8000 0.0434 0.0882
-01.8779 +0.1716 0.520
Pc < PC: There is a crossover 11001011110011000110111011100001
--------------------------------------------------------------------------
Indi:10 +05.0118 +00.5373 0.0477 0.0643
-02.5061 +0.2294 0.520
10010111100110100100
-----------------------------------------------------------------------------------------------------------------------------
---------------------
Mutation
********
====================================================
==================================
N ind K0W K1W T1 T2 Sigma
ksi
____________________________________________________________________________
_____________________
Indi:01 +02.6118 +00.4235 0.0579 0.0538
-01.1364 +0.1126
----------------------------------------------------------------------------------------------------------------------
Indi:02 +01.2941 +00.9412 0.0313 0.0361
-00.6357 +0.0664
-----------------------------------------------------------------------------------------------------------------------------
---------------------
+00.3059 0.0028 0.0882
-00.1824 +0.0191
--------------------------------------------------------------------------------------------
------------------------------------------------------
Indi:04 +02.7529 +00.5843 0.0454 0.0600
-01.2761 +0.1266
-----------------------------------------------------------------------------------------------------------------------------
---------------------
Indi:05 +05.2471 +00.3882 0.0992 0.0961
-00.7749 +0.0702
-----------------------------------------------------------------------------------------------------------------------------
---------------------
+04.7294 +00.5529 0.0938 0.0628
-01.2761 +0.1266
----------------------------------------------------------------------------------------
Indi:07 +03.1294 +00.6353 0.0489 0.0064
-01.2761 +0.1266
-----------------------------------------------------------------------------------------------------------------------------
---------------------
0.0727 0.0616
-01.2761 +0.1266
----------------------------------------------------------------------------------------------------------------------
----------------------------
Indi:09 +03.6941 +00.8000 0.0426 0.0248
-02.0490 +0.2142
-----------------------------------------------------------------------------------------------------------------------------
-
Indi:10 +02.0000 +00.5137 0.0481 0.0424
-00.9589 +0.0980
-----------------------------------------------------------------------------------------------------------------------------
---------------------
Optimization results
********
======================================================================================
N Pop K0W K1W T1 T2 Sigma ksi
Evaluation
__________________________________________________________________________
Pop:01 +05.8588 +00.5059 +00.0555 0.0075
-2.4821 +0.2719--> Acceptée
-----------------------------------------------------------------------------------------------
Pop:02 +04.2588 +00.1804 +00.0325 0.0365
-1.8625 +0.2010 --> Rejetée
--------------------------------------------------------------------------------------------------------------------
Pop:03 +04.6824 +00.4510 +00.0372 0.0448
-2.0675 +0.2158 --> Rejetée
-----------------------------------------------------------------------------------------------------------------------------
-------------
Pop:04 +04.4000 +00.1608 +00.0052 0.0534
-1.8033 +0.1992 --> Rejetée
-----------------------------------------------------------------------------------------------------------------------------
---------------------
Pop:05 +05.6235 +00.6863 +00.0590 0.0307
-2.4880 +0.2517--> Acceptée
-----------------------------------------------------------------------------------------------------------------------------
---------------------
0.0150
-2.6346 +0.2859--> Acceptée
-----------------------------------------------------------------------------------------------------------------------------
---------------------
+00.0887 0.0115
-2.2553 +0.2275--> Rejetée
-----------------------------------------------------------------------------------------------------------------------------
---------------------
+00.3137 +00.0637 0.0475
-2.1573 +0.2082--> Rejetée
-----------------------------------------------------------------------------------------------------------------------------
---------------------
+05.2706 +00.4392 +00.0263 0.0444
-2.3655 +0.2535--> Rejetée
-----------------------------------------------------------------------------------------------------------------------------
---------------------
Pop:10 +05.3176 +00.4392 +00.0579 0.0444
-2.2063 +0.2176--> Rejetée
-------------------------------------------------------------------------------------------------------------------
-------------------------------
The obtained optimizing parameters are:
K0W= +06.0000 K1W=
+00.3216 T1=+00.0516 T2= 0.0150 with Sigma=
-2.6346
Table 1 give a simulation result optimized PSS param
e-
PTIMIZED
P
ARAMETERS
BBC-720 TBB-1000
0.0445 0.0234
0.0356 0.0214
3.034 5.0142
9.548 1.506
Implementation of the rob
th
e different dynamic beh
a-
, we have created and deve
loped
a “GUI” (Graphical
MATLAB.
This GUI allows as to:
s from PSS ,H2-PSS and
lati
on results and
simulation;
system dyn
amic parameters
;
the system stability
and
robustness;
3.5. Implementation of the robust
AG-PSS-H2 under the realized GUI/ Matlab
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2022.19.9
Ghouraf Djamel Eddine,
Sayeh Abdelkader, Naceri Abdellatif
E-ISSN: 2224-3402
95
Volume 19, 2022
0 2 4 6 8
0.94
0.96
0.98
1
0 0.2 0.4 0.6 0.8 1
0
0.5
1
0 1 2 3 4 5 6 7 8
0.94
0.96
0.98
1
1.02
50
100
150
200
250
300
20
40
60
80
20
40
60
80
20
40
60
80
100
120
0 2 4 6
8
0.995
1
1.005
02468
0.96
0.98
1
1.02
0 2 4 6 8 10 12
-4
-2
0
2
4
6
8x 10-4
le temp en sec
glissment
la courbe de glissment
PSS
H2-PSS
H2-PSS-GA
0 2 4 6 8 10 12
0.82
0.84
0.86
0.88
0.9
0.92
0.94
le temp en sec
la puissance éléctromagnitique
la courbe de la puissance éléctromagnitique
PSS
H2-PSS
H2-PSS-GA
0 2 4 6 8 10 12
1.22
1.24
1.26
1.28
1.3
1.32
1.34
la courbe de l angle de charge delta
le temp en sec
delta
PSS
H2-PSS
H2-PSS-GA
0 2 4 6 8 10 12
0.97
0.975
0.98
0.985
0.99
0.995
1
1.005
1.01
1.015
le temp
la tension statorique
la courbe de tension statorique
PSS
H2-PSS
H2-PSS-GA
• Study the different operating regime (under-
excited, rated and over excited regime).
The different operations are performed from GUI that
was realized under MATLAB and shown in Figure 15.
Figure 15.The realised GUI / MATLAB
For Stability study of SMIB system we have performed
perturbations by abrupt variations of turbine torque ∆Tm of
15% at t = 0.2s,
The following results (Table 1 and Figure 16) were ob-
tained by studying the “SMIB” static and dynamic perfor-
mances in the following cases: Closed Loop System with the
conventional stabilizer PSS-FA, robust control H2-PSS and
H2-PSS optimized by GA.
We have simulated three operations: the under-excited,
the rated and the over-excited.
Our study interested in the synchronous power genera-
tors of type: TBB-200, TBB-500 BBC-720, TBB-1000 (pa-
rameters in Appendix 1) [10].
Table 1 presents the TBB -500 static and dynamic per-
formances results in (CL) with PSS ,H2-PSS and H2-PSS -
GA, for an average line (Xe = 0.3 pu), and an active power
P=0.85 p.u , for more details about the calculating parame-
ters see GUI-MATLAB in Appendix 5.
Where: α: Damping coefficient ε %: the static error,
d%: the maximum overshoot, ts: the setting time
Figure 14 show simulation results for:
a: 's' variable speed
b:'Pe' the electromagnetic power system
c:'delta' the internal angle
d:'Ug' the stator terminal voltage
for powerful synchronous generators TBB -500 with P =
0.85, Xe = 0.3, Q1 = -0.1372 (pu)
In a first step we have performed variations of the elec-
trical parametric at t = 4s (increase 100% of R). Then, we
have performed variations of the mechanical parametric at t
= 8s (lower bound 50% of inertia J)
Figure 16 .functioning system in the under-excited
regime used of TBB 500
connected to a average line with PSS , H
2
-PSS and H
2
-PSS-GA (Study of
the stability and Tests of robustness)
(c)
(d)
(b)
(a)
3.6. Simulation result and discussion
3.6.1. Stability study
3.6.2. Robustness tests
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2022.19.9
Ghouraf Djamel Eddine,
Sayeh Abdelkader, Naceri Abdellatif
E-ISSN: 2224-3402
96
Volume 19, 2022
Damping coefficient
α The static error
Q OL PSS H2-PSS
H2-PSS-GA
OL PSS H2-PSS H2-PSS-GA
-0.1372
Unstable
-1.761
-2.673
-3.3283 Unstable
1.620 -1.134 negligible
-0.4571
Unstable
-1.731
-2.593
-3.3463 Unstable
1.629 -1.141 negligible
0.1896
-0.0813 -1.855
-2.766
-3.3906 5.138 1.487 -1.167 negligible
0.3908
-0.1271 -1.759
-2.695
-3.3906 5.202 1.235 -1.029 negligible
0.5078
-0.1451 -1.470
-2.116
-2.9582 3.777 0.687 -0.504 negligible
0.6356
-0.1588 -1.442
-2.099
-2.9803 3.597 0.656 -0.467 negligible
The setting time for 5% The maximum overshoot %
Q OL PSS H2-PSS
H2-PSS-GA
OL PSS H2-PSS H2-PSS-GA
-0.1372
Unstable
1,704 rapid Very rapid 9.572 7,892 3.682 1,349
-0.4571
Unstable
1,713 rapid
Very rapid
9.487 7,847 3.482 1,323
0.1896
- 1,617 rapid
Very rapid
10,959 8,314 3.915 1,408
0.3908
- 1,706 rapid
Very rapid
10,564 7,883 3.737 1,630
0.5078
14,320 2,041 rapid
Very rapid
9,402 6,588 2.290 1,877
0.6356
14,423 2,080 rapid Very rapid 9,335 6,463 2,012 1,801
Table 2 :The “SMIB “static and dynamic performances
The electromechanical damping oscillations of parame-
ters of the synchronous power generators under-excited
mode in controllable power system, equipped by H2-PSS-
GA (Blue), PSS (Black) and H2-PSS (green) are given in
figure 14. Results of time domain simulations, with a test of
robustness (electrical uncertainties and mechanical uncer-
tainties confirm both a high effectiveness of test robust H2-
PSS-GA regulator in comparison with using the classical
regulator PID and H2-PSS . For study of the stability the
simulation results, it can be observed that the use of H2-PSS-
GA improves considerably the dynamic performances (static
errors negligible so better precision, and very short setting
time so very fast system (table 1), and we found that after
few oscillations, the system returns to its equilibrium state
even in critical situations (specially the under-excited re-
gime) and granted the stability and the robustness of the
studied system.
Graphical User Interfaces (GUIs) have been proposed
over the years as innovative tools to improve teaching at
several levels, from primary school to university, in a ple-
thora of subjects and areas of knowledge.
The main factors for successful GUI were addressed,
and important tips and hints for building a GUI using the
GUIDE tool in MATLAB were addressed. A GUI needs to
be visually appealing and easy to use, open source and have
a broad range of applicability, rather than just being focused
in education
a realized GUI for a robust GA-based on PSS using H
2
controller based an advanced Frequency technique with an op-
timal LQG controller and Kalman Filter, has been proposed
and studied
Note that, our developed GUI was exploited and ap-
plied for other study (stability and robustness) using ad-
vanced adaptive and robust Controllers. As perspective of
this work the implementation of realized GUI in real-time.
[1] LA. GROUZDEV, A.A. STARODEBSEV, S.M. OUSTINOV
"Conditions for the application of the best amortization of
transient processes in energy systems with numerical optimi-
zation of the controller parameters AVR-FA" Energy-1990-N
° ll-pp.21-25 (translated from Russian).
[2] DEMELLO F.P., FLANNETT L.N. and UNDRILL J.M., «
Practical approach to supplementary stabilizing from accele-
rating power », IEEE Trans., vol. PAS-97, pp, 1515-1522,
1978.
[3] DEMELLO F.P. and CONCORDIA C., « Concepts of syn-
chronous machine stability as affected by excitation control »,
IEEE Trans. on PAS, vol. PAS-88, pp. 316–329, 1969.
[4] S.V. SMOLOVIK « mathematical modeling Method of tran-
sient processes synchronous generators most usual and non-
traditional in the electro-energy systems "PhD Thesis State,
Leningrad Polytechnic Institute, 1988 (translated from Rus-
sian).
[5] G. STEIN and M. ATHANS “The LQG/LTR procedure for
multivariable feedback control design” , IEEE Transaction on
Automatic Control, vol. 32, No 2, 1987.
[6] NACERI A., “"Study and Application of the advanced
methods of the robust H
2
and H∞ control theory in the
AVR-PSS systems of Synchronous machines’, PhD Thesis,
SPBSPU, Saint Petersburg, Russia, 2002 (In Russian).
[7] P. KUNDUR, "Definition and Classification of power System
Stability", Draft 2, 14 January,2002
[8] P.M. ANDERSON, A. A. FOUAD "Power System control and
Stability", IEE Press, 1991.
[9] HONG Y.Y. and WU W.C., « A new approach using optimiza-
tion for tuning parameters of power system stabilizers », IEEE
Transactions on Energy Conversion, vol. 14, n°. 3, pp. 780–
786, Sept. 1999.
[10] GHOURAF D.E., “Study and Application of the advanced
frequency control techniques in the voltage automatic regula-
tor of Synchronous machines’, Magister Thesis, UDL-SBA,
2010 (In French).
[11] KWAKERNAAK H., SIVAN R. Linear Optimal Control
Systems, Wiley-Inter science, 1972.
[12] G. STEIN and M. ATHANS “The LQG/LTR procedure for
multivariable feedback control design” , IEEE Transaction on
Automatic Control, vol. 32, No 2, 1987.
[13] YURGANOV A.A., SHANBUR I.J. ‘Fuzzy regulator of
excitationwith strong action, proceeding of SPbSTU scientific
conference“Fundamental Investigation in Technical Universi-
ties”, Saint- Petersburg, 1998. (In Russian)Kwakernaak H., Si-
van R. Linear Optimal Control Systems, Wiley-Interscience,
1972.
[14] J.H. Holland, Adaptation in Natural and Artificial Systems,
University of Michigan Press, 1975.
[15] Padiyar. K.R “Power system Dynamics Stability and Control ”
B.S. publications, Second Edn. 2002.
[16] HASAN ALKHATIB ‘’Study of stability for small distur-
bances in great electrical networks: optimization of cntrol by a
metaheuristic method’’ Ph.D. Thesis, Paul Cézanne University
Aix-Marseille, 2008.
[17] K. GLOVER, J.C. DOYLE, P.P. KHARGONEKAR, B.A.
FRANCIS “State-space solutions to standard H
2
and H∞ con-
trol problems”, IEEE Trans. On A.C. 19 89, vol.34, № 8,
pp.834-847
[18] Xuebing Yang ‘Graphic User Interface Modeling and Testing
Automation’ School of Engineering and Science Victoria Uni-
versity May 2011
[19] Howard Silver ‘Creating Graphical User Interfaces with
MATLAB’ Proceedings of the spring 2013 Mid-Atlantic Sec-
tion Conference of the American Society of Engineering Edu-
cation
[20] The Math Works, Inc., “GUIDE quick start”, 2008.
4. Conclusion
References
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WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2022.19.9
Ghouraf Djamel Eddine,
Sayeh Abdelkader, Naceri Abdellatif
E-ISSN: 2224-3402
97
Volume 19, 2022
APPENDIX
1. Parameters of power system
2. Dynamics parameters calculated through GUI-MATLAB
Parame-
ters
TBB
200
TBB
500
BBC
720
TBB
1000 Notations
Generators
power
nominal
200 500 720 1000 MW
Factor of
power
nominal
0.85 0.85 0.85 0.9 p.u.
Xd
2.56 1.869 2.67 2.35
Synchronous longitudinal reactance
X
q
2.56 1.5 2.535 2.24
Synchronous
reactance
transverse
X
s
0.222 0.194 0.22 0.32
shunt inductive reactance
Statoric
X
f
2.458 1.79 2.587 2.173
Inductive reactance of the excitation circuit
Xsf
0.12 0.115 0.137 0.143
Shunt
inductive
reactance
of the excitation
circuit
Xsfd
0.0996 0.063 0.1114 0.148
Shunt
inductive
reactance of the
damping
circuit on the direct axis
R
a
0.0055 0.0055 0.0055 0.005 Statoric active resistance
R
f
0.000844 0.00084 0.00176 0.00132 Resistance of the excitation circuit (rotor)
Excitation system AVR
T1u
0.039
0.039
0.04
0.04
p.u.
Te
0.04
0.04
0.04
0.04
p.u.
K1ua
7
11
7
10
p.u.
K0ua
50
15
100
15
p.u.
T1u
0.039
0.039
0.04
0.04
p.u.
power system stabilizer PSS
T1 0.0321 0.029 0.0445 0.0234 p.u.
T2 0.054 0.0322 0.0356 0.0214 p.u.
K0W 4.074 5.011 3.034 5.0142 p.u.
K1w 5.43 6.45 9.548 1.506 p.u.
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS
DOI: 10.37394/23209.2022.19.9
Ghouraf Djamel Eddine,
Sayeh Abdelkader, Naceri Abdellatif
E-ISSN: 2224-3402
98
Volume 19, 2022
