IRJET- Analysis of 3-Phase Induction Motor with High Step-Up PWM DC-DC Converter using Matlab/Simulink

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 08 | Aug 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1539
Design and Analysis of Fuzzy & GA-PID Controllers for Optimized
Performance of STATCOM
MUKKU CHANDANPRANEETH1, DR. T. GOWRI MANOHAR2
1MTech Student, Dept. of EEE, Sri Venkateshwara University College of Engineering, Tirupati, India
2Professor, Dept. of EEE, Sri Venkateshwara University College of Engineering, Tirupati, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract:- Static Synchronous Compensator (STATCOM) is a
shunt compensating Flexible Alternating Current
Transmission System (FACTS) device capable of solving the
power quality problems at the power system. These problems
happen in milliseconds and because of the time limitation, it
requires the STATCOM that has continuous reactive power
control with fast response. One of the most common
controlling devices in the market is the Proportional-Integral-
Derivative (PID) controller. In this paper, the STATCOM is
controlled by Fuzzy Logic controllers and Genetic Algorithm
based PID controllers. The best constant values for PID
controller's parameters are obtained through trial and error,
although time consuming. The simulation results show an
improvement in current control response. These methods are
tested in MATLAB, and their results are obtained.
Key Words: Fuzzy Logic Controller, Genetic algorithm,
PID controller, STATCOM.
1. INTRODUCTION
Reactive power control is a critical consideration in
improving the power quality of power systems. Reactive
power increases transmission losses, degrades power
transmission capability and decreases voltage regulation at
the load end. In the past, Thyristor – Controlled Reactors
(TCR) and Thyristor - Switched Capacitors were applied for
reactive power compensation. However, with the increasing
power rating achieved by solid-state devices, the Static
Synchronous Compensator (STATCOM)istakingplaceasone
of the new generation flexible AC transmission systems
(FACTS) devices. It has been proven that the STATCOM is a
device capable of solving the power quality problems.Oneof
the power quality problems that always occur at the system
is the three phase fault caused by short circuit inthe system,
switching operation, starting large motors and etc. This
problem happens in milliseconds and because of the time
limitation, it requires the STATCOM that has continuous
reactive power control with fast response [3].A STATCOM
provides better dynamic performance and minimal
interaction with the supply grid. The STATCOM is a shunt
connected device. The STATCOM consists of voltage source
inverter such as Gate Turn off (GTO) Thyristor, a DC link
capacitor and a controller [4]. Asa promisingtechnology,the
control of STATCOM has been discussed in many literatures
[5]-[8]. Many of the methodsfocuson decouplingthesystem
variables and designing PI controllers. ASTATCOM is a
Multiple Input Multiple Output (MIMO) system. It is not
possible to totally decouple the system variables. Therefore,
the control performance may sometimes be poor. Other
control methodsapply state feedback controltechniques[7],
[8], however, very little detail is given in the literature about
how to choose the optimal parameters. Some control
methods apply state feedback control techniques [3], [10].
For the pole placement method, the controller is based on
dynamic model rather than phasor diagram which produces
a fast response to the system [3], [10].In order to increase
the stability of the system and damping response which
makes the inverter in the STATCOM to inject voltage or
current to compensate the three phase fault [8]. This type of
controller is able to control the amount of injectedcurrentor
voltage or both from the STATCOM inverters. The PI
controller dependson the reactive power, whichis theinput
to the controller for injection of the currents from the
STATCOM and cause the controller to have slow response
[3]. In PID controller method, it is able to give a fast
response. In this paper, a PID control method [10] for
STATCOM control is introduced. This method uses a PID
controller, with assistances of genetic algorithm, in the
STATCOM current control loop. The new methodis tested in
Matlab, and their results are obtained. The remainder of the
paper is organized asfollows. Section 2 describesmodelling
of STATCOM. The design of the proposed control algorithm
(PID) is detailed in Section 3, Genetic algorithm is shown in
Section 4. The computer simulation results are presented
and discussed in Section 5. Finally, Section 6 concludes this
paper.
2. MODELING OF STATCOM
In designing the PID controllers, the state space
equations from the STATCOM circuit must be introduced.
The theory of dq transformation of currentshasbeenapplied
in the circuit, which makes the d and q components as
independent parameters. Fig. 1 showsthe circuit diagramof
a typical STATCOM. The STATCOM is connected in shunt
with the power system and the capacitor is used to supply
the voltage to the inverter to solve the power quality
problems. One convenient way for studying balanced three-
phase system (especially in synchronousmachineproblems)
is to convert the three phase voltages and currents into
synchronous rotating frame by abc/dq transformation.
The benefits of such arrangement are: The control
problem is greatly simplified because the system variables
become DC values under balanced condition; multiple
control variables are decoupled so that the use of classic
control method is possible, and even more physicalmeaning

for each control variable can be acquired. Equations (1) to
(4) give the mathematical expression of the STATCOM
shown in Fig. 1, [6], [11]. The variable ω istheangularpower
frequency, and subscripts d, q represent variablesinrotating
dq− coordinate system.
Fig. 1 STATCOM system configuration
Given a linear system,
Writing equations (1) and (2) in the state space format as
(5), the corresponding matrix can be found as,
Where the states x, the inputs u, and the output y,
Fig. 2 STATCOM with controllers.
There are different processes for different
composition of proportional, integral and differential.
The duty of control engineering is to adjust the
coefficientsof gain to attain the error reductionanddynamic
responses simultaneously. The PID controlling Coefficients
are the same as (5).
For PI and PD controllers, the coefficientgain,which
is not required, is taken as zero. The above equations can
only be applied for complex pole of s1, in case s1 is real, the
zero controller PD, (Z0 = KP / KD), and the zero controller PI,
(Z0 = KI / KD), is definite and then their gain are obtained to
satisfy the angle criteria and size. In the design of PID
controller the amount of KI is identified to reach to an
intended error in steady state. In PID controller design, KP,
KI, and KD, related to the closed loop feedbacksystemwithin
the least time is determined and requires a long range of
trial and error. PID control is a linear control methodology
with a very simple control structure (see Fig. 2).
This type of controller operatesdirectlyontheerror
signal, which is the difference between the desired output
and the actual output, and generatesthe actuationsignalthat
drives the plant. PID controllers have three basic terms:
proportional action, in which the actuation signal is
proportional to the error signal, integral action, where the
actuation signal is proportional to the time integral of the
error signal, and derivative action, where the actuation
signal is proportional to the time derivative of the error
signal. To design a particular control loop, the values of the
three constants (KP, KI, and KD) have to be adjusted so that
the control input providesacceptable performance fromthe
plant. In order to get a first approach to an acceptable
solution, there are several controller design methods that
can be applied. For example, classical control methodsinthe
frequency domain or automatic methods like Ziegler–
Nichols, which is the most well-known of all tuning PID
methodologies. Although these methods provide a first
approximation, the response producedusuallyneedsfurther
manual retuning by the designer before implementation.

3. FUZZY LOGIC CONTROLLER
Fuzzy logic usesfuzzy set theory, inwhichavariable
is member of one or more sets, with a specified degree of
membership. Fuzzy logic allow us to emulate the human
reasoning process in computers, quantify imprecise
information, make decision based on vague and in complete
data, yet by applying a “defuzzification” process, arrive at
definite conclusions[2]. The FLC mainly consists of four
blocks
 Fuzzification
 Inference
 Defuzzification
 Rule Base
Fig. 3 Block Diagram of a Fuzzy Logic Controller
3.1 Fuzzification
It is the process that converts conventional
expressions to fuzzy membership functions. Here triangular
membership functions with five levels are taken with
membership functions of error and change in error.Thebest
performance of the membership functions can be
determined by the trial and error method. The fivelevelscan
be described as Negative Big (NB), Negative Small (NS),Zero
(ZO), Positive Big (PB), and Positive Small (PS).
3.2 Inference
Fuzzy Inference System is the key unit of a fuzzy
logic system having decision making as its primary work. It
uses the “IF…THEN” rules along with connectors “OR” or
“AND” for drawing essential decision rules. The output from
Fuzzy Inference System is always a fuzzy set irrespective of
its input which can be fuzzy or crisp set.
3.3 Deffuzification
It is the process that converts fuzzy terms into
conventional expressions quantified by real-valued
functions.
3.4 Rule Base
The each input variable has five membership
functions and each membership function has five levels.
Therefore a total of twenty five rules can be formed. The
Mamdani’s type fuzzy reference system is employed in the
rule editor to get the output fuzzy set for every rule.
Table.1 Fuzzy Logic Controller Rule Base
e/ce NB NS ZO PS PB
NB NB ZO ZO ZO NB
NS NB NS NS NS NB
ZO NS NB NB PS NB
PS PS PS NB PB PS
PB PB PB NB NB PB
4. GENETIC ALGORITHM
GA as a powerful and broadly applicable stochastic
search and optimization techniques is perhaps the most
widely known types of evolutionary computation method
today. In this paper, the GA is employed to find the best KP,
KI, and KD parameters. Genetic Algorithms are heuristic
search algorithms based on the mechanics of natural
selection, genetics and evolution. [11], [12].
4.1 Genetic algorithm procedure
The main procedure of applying GA’s to search the
optimum parameters of the controller’s include:
4.1.1 Encoding:
The first step in applying GA’s to the selection of
STATCOM controller parameters is Encoding, which maps
the parameters of the controller’s into a fixed-length string.
4.1.2 Fitness Computation:
According to the comprehensive design objectives
as mentioned above.
4.1.3 New Population Production:
New populations are created using three operators:
Reproduction, Crossover and Mutation. Reproduction is a
process in which individual strings are copied according to
their fitness value. Reproduction directs the search toward
the best existing individuals but does not create any new
individuals. The main operator working on the parents is
Crossover, which happens for a selected pair with a
crossover probability. Multi-point crossover has been
applied to solve combinations of features encoded on
chromosomes. Although Reproduction and Crossover
produce many new strings, they do not introduce any new
information into the population. As a source of new bits,
mutation is introduced and is applied with a lowprobability.

4.1.4 Stopping Criterion:
If all of the objectives are met, the generation cycles will
terminate. Otherwise, go to step (2), and compute thefitness
for each population.
4.1.5 Decoding:
This process converts binary alphabets into digital
numbers, which givesmeaning to the strings,afterwhichthe
controller parameters are finally determined.
Fig. 4 Schematic of GA operation.
4.2 PID controller design by genetic algorithm
The target function is as follows,
(11)
That tr is rise time, ts is settling time, Mp is overshoot and
Ess is steady state error.
The genetic algorithm flowchart for PID controller is shown
in Fig.5,
The members of every individual are KP, KI and KD,
Population size M=20,
Crossover rate PC=0.9,
Mute rate Pm=0.01,
The number of generation is 1000.
Fig. 5 Genetic algorithm flowchart for PID controller.
5. THE RESULT OF SIMULATION
The system parameters chosen are listed below [9]:
Source line-line voltage: 460 V
DC linkage voltage dc: 800 V
Rated power: 140 KVA
Frequency: 50Hz
Line resistance R: 2mΩ
Line inductance L: 400uH
DC linkage capacitance C 7.8mF
Therefore A, B matrices,

The STATCOM open loop response will be in the
form of Fig. 6. To optimize the id, PID controllermethodwith
GA is used.
Fig. 6 STATCOM open loop response.
5.1 The result of simulation with PID controller
With various KP, KI, and KD designs, the following
results the current response of STATCOM are obtained, The
KP, KI, and KD in PID control method in the form of manual
and genetic algorithm are presented in Table.1, and their
outcomes are illustrated in Fig. 7~9.
Fig. 7 STATCOM response with PID controllers
(PID1,PID2,PID3).
Fig. 8 STATCOM response with PID controllers
(PID4,PID5,PID6).
By applying genetic algorithm for placement
optimization of the system poles for the improvement of
STATCOM response, we obtain the conclusions in Fig.7
Fig. 9 STATCOM response of PID control methods with GA.
With regard to the results presented in Fig. 9, it is
observed that by applying genetic algorithm, PID controller
response will improve.
Table.2 PID controller's parameters
KP KI KD
PID 1 0.728 151.64 0
PID 2 1.1 105 0
PID 3 11 105 0
PID 4 0.5 105 0
PID 5 126.8 137.5 0.001
PID 6 26.81 37.55 0.605
PID-GA 782.189 1.65 0
5.2 When sensor noise or system disturbance exist
Although these control methodsshow usalmostthe
same good performance on the operating point, it is
necessary to investigate their robustness. So we need to see
the system performance when change from its operating
point (id=1 pu). First we make the id as a step change from 1
pu to 1.5 pu (increase50%).
Fig. 10 PID controller(1,2,3) response to a change in the
reference active current id from 1 pu.

Fig. 11 PID controller(4,5,6) response to a change in the
5.3 The result of simulation with FUZZY controller
Fig. 12, show the system response by using Fuzzy
control method. In GA-Fuzzy loop we set id have stepchange
from 1 pu to 1.5 pu is less than 1/Fs sec.
Fig. 12 Fuzzy controller response to a change in the
Fig. 13 Comparison of STATCOM response of GA-PID &
Fuzzy controllers
6. CONCLUSION
The reduction of output current ripple of the
STATCOM is very important. One of the most common
controlling devices in the market is the PID controller and
Fuzzy Logic Control is able to control the dynamic behavior
of the STATCOM. This paper presentsa novel designmethod
for determining the PID controller parameters and Fuzzy
logic Design. The results can show that the proposed Fuzzy
Controller method can obtain highqualitysolutionwithgood
computation efficiency. Therefore, the proposedmethodhas
robust stability and efficiency, and can solve the controller
parametersmore easily and quickly. The system responseis
reduced by the introduction of Fuzzy Self Tuningcontroller.
While considering the STATCOM currents, the Fuzzy Self
Tuning controller plays an important role by increasing the
injection ratio. The results of simulation prove the
improvement of the performanceofProposedFuzzymethod.
REFERENCES
[1] Saeid Eshtehardiha, Mohammad Bayati poodeh, Arash
Kiyoumarsi., “Optimized PerformanceofSTATCOMwith
PID Controller Based on Genetic Algorithm,”
International Conference on Control, Automation and
Systems 2007.
[2] Abhijith Augustine, Eril Paul,,“ Voltage Regulation of
STATCOM using Fuzzy self-Tuning PI Controller,”
International Conference on Circuit, ICCPCT 2016.
[3] Xing, L., “A Comparison of Pole Assignment and LQR
Design Methods for Multivariable Control for
STATCOM,” MSc. dissertation, Florida State University,
2003.
[4] Sen, K, K., “Statcom-Static Synchronous Compensator:
Theory, Modeling and Applications,” IEEE Power
Engineering Society, 1177-1183, 1999.
[5] Pablo, G. G., Aurelio, G. C., “Control System fora PWM-
based STATCOM,” IEEE TransactionsonPowerDelivery,
vol. 15, 4, 2000.
[6] Blasko, V., Kaura, V., “A New Mathematical Model and
Control of a Three-Phase AC-DC Voltage Source
Converter,” IEEE TransactionsonPowerElectronics,vol.
12, 1, 1997.
[7] Lehn, P. W., Iravani, M. R., “Experimental Evaluation of
STATCOM Closed Loop,” IEEE Transactions on Power
Delivery, vol. 13, 4, 1998.
[8] Rao, P., Crow, M. L., Yang, Z., “STATCOM Control for
Power System Voltage Control Applications,” IEEE
Transactions on Power Delivery, vol. 15, 4, 1311-
1317, 2000.
[9] Ghosh, A., Jindal, A.K. and Joshi, A, “Inverter Control
Using Output Feedback for Power Compensating

Devices,” Conference on Convergent Technologies for
Asia Pacific Region, 48-52, 2003.
[10] Mariun, N., Hizam, H., Noor Izzri, AW.,Aizam,Sh.,
“Design of the Pole Placement Controller for D-
STATCOM in Mitigating Three Phase Fault,” IEEE PES
2005 Conference, 349-355,2005.
[11] Ren, W., Qian, L., Cartes, D., Steurer, M., “A
Multivariable Control Method in STATCOM Application
for Performance Improvement”,IEEE Trans, vol. 40, 3,
2246-2250, 2005.
[12] Astrom, K., Hagglund, T., “PID controllers: theory
design and tuning,” Instrument Society of America,
Research Triangle Park, NC, USA, 1995.
[13] Goldberg, D. E., Genetic Algorithms in Search,
Optimization and Machine Learning, Wesley, 1989.
[14] Davis, L., Handbook of Genetic Algorithm,NewYork,
1991.

IRJET- Analysis of 3-Phase Induction Motor with High Step-Up PWM DC-DC Converter using Matlab/Simulink

More Related Content

What's hot (18)

Similar to IRJET- Analysis of 3-Phase Induction Motor with High Step-Up PWM DC-DC Converter using Matlab/Simulink (20)

More from IRJET Journal (20)

Recently uploaded (20)

IRJET- Analysis of 3-Phase Induction Motor with High Step-Up PWM DC-DC Converter using Matlab/Simulink