Design OFAVR Using Genetic Algorithm and PID Controller

Middle-East Journal of Scientific Research 20 (12): 2075-2078, 2014
ISSN 1990-9233
© IDOSI Publications, 2014
DOI: 10.5829/idosi.mejsr.2014.20.12.21845
Design OFAVR Using Genetic Algorithm and PID Controller
1
1
R. Udayakumar, 2G. Saritha and 3T. Saravanan
School of Computing Sciences, Bharath University, Chennai, India
2
Department of EEE, Sathyabama University, Chennai, India
3
Department of ETC, Bharath University, Chennai-47, India
Abstract: With the rise of electricity demand, the secure and economic operation of power systems requires
improved and innovative methods of control. Recently, Genetic Algorithm (GA) is a new paradigm for
optimization based on biological evolution provides a powerful search technique for power systems.
This paper deals with PID controller tuning of AVR of an isolated power system using GA. The simulation
results from other intelligent controller techniques like fuzzy and neural are also presented. The superiority of
the performance with proposed GA based PID controller is demonstrated with simulation results.
Key words:Automatic Voltage Regulator (AVR) Genetic Algorithms (GA) Proportional-Integral-Derivative
controller (PID) Integral-Time Average Error (ITAE) Integral Absolute Error (IAE) Integral
Squared Error (ISE)
INTRODUCTION
Due to the increasing size and complexity of electric
power systems, they are often operated with a lower
stability margin. Therefore, the stability enhancement is
one of the most important issues for the reliable operation
of electric power system. Excitation control is well known
as one of the effective means to enhance overall power
system stability [1].
The generator excitation system maintains generator
voltage and controls the reactive flow using an AVR.
The role of AVR is to hold the terminal voltage magnitude
of a synchronous generator at a specified level. Hence the
stability of the AVR would seriously affect the security
level of the power system. Some modern voltage
regulators utilize PID controllers for stabilization. Due to
model uncertainties, problems in tuning high order
systems and delays the conventional PID controller
schemes do not yield high performance [1].
The growth in size and complexity of electric power
systems along with increase in power demand has
necessitated the use of intelligent systems that combine
knowledge, techniques and methodologies from various
sources for the real-time control of power systems.
The intelligent controllers using fuzzy and neural
methods are implemented to improve the dynamic
performance of AVR in an isolated power system [2].
Fuzzy controller will make an intelligent decision on
the amount of field current that should be applied to the
generator by considering two inputs namely voltage and
change in voltage with seven linguistic variables, 49 rules
and mamdani method produces the output in order to
keep voltage at its rated value and provides a satisfactory
performance for AVR in an isolated power system [3].
Neural controller for AVR in an isolated power
system is implemented by using multilayer feedforward
network either with Radial Basis Function (RBF) or with
two-layer sigmoid/linear Back Propagation (BPN) gradient
descent learning algorithm. Its training data set is taken
from fuzzy controller. The dynamic performance proves to
be satisfactory than Fuzzy controller [4].
GA represents a heuristic search technique based
on the evolutionary ideas of natural selection and
genetics. Here the genetic algorithm is used for PID
tuning of AVR model [5]. The evaluation function is a
function of error and time [6, 7]. The Integral Absolute
Error (IAE), Integral Squared Error (ISE) and Integral-Time
Average Error (ITAE) are used. ITAE overcomes the
Corresponding Author: R. Udayakumar, School of Computing Sciences, Bharath University, India.
2075
Middle-East J. Sci. Res., 20 (12): 2075-2078, 2014
Fig 1.0: Block Diagram of an Automatic Voltage Regulator
Table 1: System Parameters for Avr
Gain
Amplifier
Exciter
Generator
Sensor
KA=10
KE=1
KG=0.8
KR=1
Time constant in sec
The Algorithm for this Study Is Given Below:
=0.1
=0.4
G=1.4
R=0.05
Initialize a population of chromosomes.
Evaluate each chromosome in the population.
Create new chromosome by Crossover and Mutation
operators.
Evaluate the new chromosomes for intermediate
population.
Compare intermediate population with last
population and chose the best chromosomes from
last and intermediate population for new population.
Change the name of new population as last
population. If time is finished, stop and return the
best chromosome, if not, go to step number 3.
A
E
disadvantages of other methods such as long-duration
transients and overshoots. The optimal adjustment of
the PID controller gains with ITAE are investigated and
the enhancement in the dynamic performance is verified
through simulation results.
Automatic Voltage Regulator Model (Avr Model):
The primary means of generator reactive power control
is the generator excitation control using automatic
voltage regulator (AVR) [1]. Fig 2.1 shows the schematic
diagram for automatic voltage regulator (AVR).
An increase in the reactive power load of the
generator is accompanied by a drop in the terminal
voltage magnitude. The voltage magnitude is sensed
through a potential transformer on one phase.
This voltage is rectified and compared to a dc set point
signal. The amplified error signal controls the exciter
field and increases the exciter terminal voltage. Thus, the
generator field current is increased, which results in an
increase in the generated emf. The reactive power
generation is increased to a new equilibrium, raising the
terminal voltage to the desired value [1]. The controller
parameters for AVR taken for study are shown in Table 1.
GA Based Pid Tuning for AVR Model: Genetic
Algorithms (GAs) are adaptive search procedures for
optimization and learning [5]. The concepts of the
algorithms are loosely based on natural population
genetics.
The conventional method to tune the gains of PID
controller with numerical analysis is tedious and time
consuming. Genetic Algorithm, a random search
technique provides a powerful way for larger search
space and they converge fair rapidly to near optimal
solutions [6].
Initialization: The initial population for this case is
randomly generated in binary code. So each chromosome
is described by a binary string, then these binary strings
are changed to decimal values for the evaluations [6, 7].
The parameters of the PID tuning used here are Kp,
Ki and Kd, the Proportional-Integral-derivative gain
respectively. Each parameter will be called a gene and
the combination of genes in one string such as ‘Kp-Ki-Kd’
form a chromosome. The initial population size of 80 is
chosen for study.
Evaluating the Fitness Function: When a GA is applied,
the optimization problem should be converted to a
suitably described function.The corresponding function
is called “fitness function”. The fitness function is the
performance index of a GA to resolve the viability of
each chromosome. The higher the fitness value, the
better the system's performance [5, 6].
For AVR, the voltage error is given by,
Ve (s) = VR(S) - V S (s )
(1)
In general, the PID controller design method using
the integrated absolute error (IAE), or the integral of
squared-error (ISE), or the integral-time-averaged error
2076
Middle-East J. Sci. Res., 20 (12): 2075-2078, 2014
(ITAE) is often employed in control system design
because it can be evaluated analytically in the frequency
domain. The three integral performance criteria in the
frequency domain have their own advantages and
disadvantages.
(2)
IAE =
∫
∞
0
ISE =
∫
∞ 2
0 e ( t )dt
(3)
∫
(4)
ITAE =
e (t ) dt
∞
0 t
e(t) dt
Where e is the voltage error in AVR model
The disadvantage of the IAE and ISE criteria is that
its minimization can result in a response with relatively
small overshoot but a long settling time because the ISE
performance criterion weights all errors equally
independent of time. Although the ITAE performance
criterion can overcome the disadvantage of the ISE
criterion, the derivation processes of the analytical
formula are complex and time-consuming which can be
made easy with GA search technique.
Genetic Operations: The population is then transformed
in stages to yield a new current population for next
iteration. The transformation is usually done three stages
by simply applying the following genetic operators:
(1)Reproduction (2)Crossover and (3)Mutation [5].
Reproduction is the operator carrying old strings through
into a new population, depending on the fitness value.
Strings with high fitness values obtain a larger number of
copies in the next generation. Crossover is an effective
way of exchanging
information and recombining
segments from high fitness individuals. Crossover is
an operator which
simply exchanges segments
between pair of parent strings. Mutuation is an
operator which alternatively changes one or more
elements on a string [5,6]. Here the probabilities of
Geometric reproduction, Arithmetic cross-over and
uniform mutation are 0.08,4 and 8 are chosen
respectively.
If the goal is not achieved in the current generation
after the genetic operations, chromosomes of the current
generate will go through to the next generation. The GA
operation will repeat the procedure until the requirement
is achieved.
New Population: After the selection of new chromosomes
and generation of the intermediate population, this
population should be compared with the last generation
and the best chromosome of each population should be
selected for the next step (6). The pool population which
is used in this GAs has a size of about 220. It contains all
the copies of each chromosome that are created during
one generation. The size of 220 is chosen for more
accuracy.
Simulation Results: The simulation results with
conventional PID, Fuzzy and Neural intelligent controller
techniques for AVR model of an isolated power system is
implemented and results are presented. MATLAB 7.0 is
used for simulating the system. The Genetic Algorithm
(GA) with various evaluation functions is implemented.
The enhancement in dynamic response is observed with
ITAE based PID tuning using GA.
With IAE method of PID tuning using GA the values
are: K d = 86.94290,K p = 92.90549, K i = 99.8934
With ISE method of PID tuning using GA the values
are: Kd = 88.35187 K p = 88.04131 K i = 99.7125
With ITAE method of PID tuning using GA the
values are:
Kd = 20.28485 K p = 39.90599 K i = 57.4063
The PID tuning GA-ITAE yields better response
with lower search bounds.
Fig 2.0: AVR model without any controller
Figure 3.0 AVR model with conventional PID controller
2077
Middle-East J. Sci. Res., 20 (12): 2075-2078, 2014
REFERENCES
1.
2.
3.
Fig 4.0:AVR model with Fuzzy logic and Neural network
controller
4.
5.
6.
Fig 5.0:AVR model with genetic algorithm controller with
different objective functions
7.
CONCLUSION
The results presented in this paper shows that
genetic algorithm techniques provides a method for
designing more efficient AVR controllers. It has enhanced
dynamic performance when compared with other methods.
It proves to be simple, robust and provide near optimal
results.
2078
Hadi Saadat, 2002. Power System Analysis. Tata
McGraw-Hill Edition,India.
Driankov, D., 1996. An Introduction to Fuzzy
Control. Narosa Publishing House.
Mangla, A. and J. Nanda, 2004. “Automatic
Generation Control of an Interconnected HydroThermal System Using Conventional Integral and
Fuzzy Logic Controller”, IEEE International
Conference on Electric Utility Deregulation,
Restructuring
and
Power
Technologies
(DRPT2004), Hong Kong, April, pp: 372-377.
Donald
C.
Wunsch
II,
Ganesh
K.
Venayagamoorthy and Wenxin Liu, 0000.
“Adaptive Neural Network Based Power System
Stabilizer Design” Department of Electrical and
Computer Engineering., University of Missouri,
Rolla, USA.
Goldberg, D.E., 1989. Genetic Algorithms in
Search Optimization and Machine Learning,
Addison-Wesley, Reading Mass.
Ravindra Singh, 2004. “A Novel Approach for
Tuning of Power System Stabilizer Using Genetic
Algorithm”, A Thesis Submitted for the Degree of
Master of science in Indian Institute of Science,
Bangalore, India July, pp: 1-102.
Besmi, M.R. and J.W. Finch, 1995. “Genetic
Algorithms applied to a Power System Stabilizer”,
Genetic Algorithms in Engineering Systems:
Innovations and Applications., Conference
Publication No. 414, IEE, 12-14th September,
pp: 100-105.

Download Report