Automatic generation-control-of-interconnected-power-system-with-generation-rate-constraintsby-hybrid-neuro-fuzzy-approach

World Academy of Science, Engineering and Technology
International Journal of Electrical, Electronic Science and Engineering Vol:3 No:4, 2009

Automatic Generation Control of Interconnected
Power System with Generation Rate Constraints
by Hybrid Neuro Fuzzy Approach
Gayadhar Panda, Sidhartha Panda and Cemal Ardil

power systems have been reported over the past six decades
[1-5].
A number of control strategies have been employed in the
design of load frequency controllers in order to achieve better
dynamic performance. Among the various types of load
frequency controllers, the most widely employed is the
conventional proportional integral (PI) controller [8, 9].
Conventional controller is simple for implementation but takes
more time and gives large frequency deviation. A number of
state feedback controllers based on linear optimal control
theory have been proposed to achieve better performance
[10.11]. Fixed gain controllers are designed at nominal
operating conditions and fail to provide best control
performance over a wide range of operating conditions. So, to
keep system performance near its optimum, it is desirable to
track the operating conditions and use updated parameters to
compute the control. Adaptive controllers with self-adjusting
gain settings have been proposed for LFC [13-15].
AGC approaches may be classified into two categories as
follows:
1. Energy storage system: Examples are pumped storage
system, superconducting magnetic energy storage system,
battery energy storage system etc. [6, 7].
2. Control strategy: This category focuses on the design of
an automatic generation controller to achieve better
dynamic performance [8-15].
Literature survey shows that only a few investigations have
been carried out using FLC [16, 18]. Among the
aforementioned controllers, the most widely employed one is
the fixed gain controller, like integral controller or PI
controller due to its low cost and high reliability in operation.
Fixed gain controllers are designed at nominal operating
points and may no longer be suitable in all operating
conditions. For this reason, some authors have applied the
variable structure control [19, 20] to make the controller
insensitive to the system parameter changes. However, this
method requires the information of the system states, which
are very difficult to predict and collect completely.
In view of the above discussion, the following are the main
objectives of the present work:
1. To apply hybrid neuro-fuzzy (HNF) controller for the
automatic generation control and tested for the two area
interconnected system with and without GRC.

International Science Index 28, 2009 waset.org/publications/7268

Abstract—The design of Automatic Generation Control (AGC)
system plays a vital role in automation of power system. This paper
proposes Hybrid Neuro Fuzzy (HNF) approach for AGC of two-area
interconnected reheat thermal power system with the consideration of
Generation Rate Constraint (GRC). The advantage of proposed
controller is that it can handle the system non-linearities and at the
same time the proposed approach is faster than conventional
controllers. The performance of HNF controller has been compared
with that of both conventional Proportional Integral (PI) controller as
well as Fuzzy Logic Controller (FLC) both in the absence and
presence of Generation Rate Constraint (GRC). System performance
is examined considering disturbance in each area of interconnected
power system.

Keywords—Automatic Generation Control (AGC), Dynamic
Response, Generation Rate Constraint (GRC), Proportional Integral
(PI) Controller, Fuzzy Logic Controller (FLC), Hybrid Neuro-Fuzzy
(HNF) Control, MATLAB/SIMULINK.
I. INTRODUCTION

M

EGAWATT frequency control or Automatic Generation
Control (AGC) problems are that of sudden small load
perturbations which continuously disturb the normal operation
of an electric energy system. The analysis and design of
Automatic Generation Control (AGC) system of individual
generator eventually controlling large interconnections
between different control areas plays a vital role in automation
of power system. The purpose of AGC is to maintain system
frequency very close to a specified nominal value to maintain
generation of individual units at the most economical value
and to keep the correct value of the line power between
different control areas. Many investigations in the area of
Load Frequency Control (LFC) problem of interconnected

_____________________________________________
Gayadhar Panda is working as Professor in the Department of Electrical
and Electronics Engineering, National Institute of Science and Technology,
Berhampur, Orissa, INDIA. (e-mail: p_gayadhar@ryahoo.co.in).
Sidhartha Panda is working as Professor in the Department of Electrical
and Electronics Engineering, National Institute of Science and Technology,
Berhampur, Orissa, INDIA. (.e-mail: panda_sidhartha@rediffmail.com ).
Cemal Ardil is with National Academy of Aviation, AZ1045, Baku,
Azerbaijan, Bina, 25th km, NAA (e-mail: cemalardil@gmail.com).

1



Fig. 1 Transfer function model of two-area reheat thermal system

2.

3.

A. Modeling of Two Area system with GRC

To develop MATLAB/SIMULINK based adaptive
networks that are functionally equivalent to fuzzy
inference systems.
To compare the performance of the Hybrid Neuro-Fuzzy
(HNF) controller with conventional PI and Fuzzy Logic
Controller (FLC).

In a power system having steam plants, power generation
can change only at a specified maximum rate. As described in,
adding limiters to the governors can restrict the generation rate
for the steam plants. A typical value of the Generation Rate
Constraint (GRC) for thermal units is considered as 3%/min.,
i.e. GRC for the kth subsystem is .

II. SYSTEM MODEL
.

PGk t

Investigations have been carried out on a two equal area
reheat thermal power system considering GRC (Fig.1). A step
load perturbation of 1% of nominal loading has been
considered in area-1. Here, the tie-line power deviations can
be assumed as an additional power disturbance to any area k
[12]. For the load frequency control, the proportional integral
controller is implemented.

0.0005 p.u.MW / s

(1)

Two limiters, bounded by ± 0.0005 are used within the
automatic generation controller to prevent the excessive
control action. The generation rate constraints for all the areas
are taken into account by adding limiters to the turbines as
shown in Fig. 2.
B. Automatic Controller
The task of load frequency controller is to generate a
control signal Ui that maintains system frequency and tie-line
interchange power at predetermined values. The block
diagram of the PI controller is shown in Fig. 3. The control
input Ui is constructed as follows:

Fig. 2. Nonlinear Turbine Model with GRC

2


T
K i 0

U i

T
K i 0

ACE i dt

P Tiei

Bi

B. Fuzzyfier

F i dt

(2)

The first step in designing a fuzzy controller is to decide
which state variables represent the system dynamic
performance must be taken as the input signal to the
controller. Fuzzy logic uses linguistic variables instead of
numerical variables. The process of converting a numerical
variable (real number or crisp variables) into a linguistic
variable (fuzzy number) is called Fuzzification. System
variables, which are usually used as the fuzzy controller inputs
includes states error, state error derivative, state error integral
or etc. In power system, based on previous experience, Area
Control Error (ACE) and its derivative (d(ACE)/dt) are chosen
to be the input signals of fuzzy AGC.
The membership function is a graphical representation of
the magnitude of participation of each input. There are
different memberships functions associated with each input
and output response. In this study, we use the trapezoidal
membership function for input and output variables. The
number of membership function determines the quality of
control which can be achieved using fuzzy controller. As the
number of membership function increases, the quality of
control improves. As the number of linguistic variables
increases, the computational time and required memory
increases. Therefore, a compromise between the quality of
control and computational time is needed to choose the
number of linguistic variables. For the AGC study, five
linguistic variables for each of the input and output variables
are used to describe them, as in the following Table I.

Taking the derivative of equation (2) yields
.

Ui

K i ACE i

Ki

PTiei

Bi

Fi

(3)


Fig. 3. Conventional PI Controller Installed on ith Area
Automatic Generation Control gives poor performance
under different generator loading conditions, and hence the
need for Hybrid Neuro-Fuzzy (HNF) Logic base Controller
[21] arises. In order to accomplish a satisfactory frequency
and tie-line power characteristic over a wide range of
operating points, area control error (ACE) and its derivative
(d(ACE)/dt) were taken as the input signals to the Fuzzy
controller. It is known that these variables have significant
effects on generators. A modification of the integral gain (K11
and K12) on the unit is also used to enhance the stability of the
system. The stabilizing signals are computed using the
standard fuzzy membership function depending on these
variables. The performance of the proposed neuro-fuzzy logic
base Automatic Generation Control (AGC) is being
investigated to have better control.

TABLE I FUZZY LINGUISTIC VARIABLES

NB
NS
ZE
PS
PB

Negative Big
Negative Small
Zero
Positive Big
Positive Small

III. HYBRID NEURO FUZZY (HNF) MODEL

TABLE II RULE TABLE FOR FUZZY CONTROLLER

A neuro-fuzzy system is a fuzzy system that uses a learning
algorithm derived from neural network theory to determine its
parameters (fuzzy sets and fuzzy rules) by processing data
samples. In recent years, hybrid neuro-fuzzy(HNF) approach
[21] has considerable attention for their useful applications in
the fields like control, pattern recognition ,image processing,
etc. In all these applications there are different neuro-fuzzy
applications proposed for different purposes and fields. Hybrid
neuro-fuzzy results are obtained from fusion of neural network
and fuzzy logic.

ACE
NB
NS
NB NB
NB NB
NS
NS
ZE
PS
ZE
ZE

d(ACE)/dt

NB
NS
ZE
PS
PB

ZE
NS
NS
ZE
PS
PS

PS
NS
ZE
PS
PB
PB

PB
ZE
ZE
PS
PB
PB

C. Rule base and inference engine:
The rules are in the following format. If error is Ai, and
change in error is Bi then output is Ci. Here the “if” part of a
rule is called the rule-antecedent and is a description of a
process state in terms of a logical combination of atomic fuzzy
propositions. The “then” part of the rule is called the rule
consequent and is a description of the control output in terms
of a logical combination of fuzzy propositions. The rule table
for the designed fuzzy controller is given in the Table 2. For
two inputs and 5 membership functions, we have derived 25

A. Fuzzy Controller Modeling
There are three principal elements to a fuzzy logic
controller:
Fuzzification module (Fuzzifier)
Rule base and Inference engine
Defuzzification module (defuzzifier)
The general algorithm for a fuzzy system designer can be
synthesized as follows:

3


rules. From the rule table, the rules are manipulated as
follows; if the ACE is NB, and its derivative is NB then output
is NB.

5.
6.

D. Defuzzyfication
The reverse of Fuzzification is called Defuzzification. The
use of Fuzzy Logic Controller (FLC) produces required output
in a linguistic variable (fuzzy number). According to real
world requirements, the linguistic variables have to be
transformed to crisp output. Centre of gravity method is the
best well-known defuzzification method and used in this
research work. Sugeno type of defuzzification method is
adopted in this work. It obtains the center of area occupied by
the fuzzy set. It is given by the expression:

X

( x) xdx
( x)dx

IV. SIMULATION RESULTS
The proposed scheme utilizes sugeno-type fuzzy inference
system controller, with the parameters inside the fuzzy
inference system decided by the neural-network back
propagation method [22]. The ANFIS is designed by taking
ACE and rate of change of ACE as inputs. The
MATLAB/SIMULINK model of two-area reheat thermal
system with HNF controller is shown in Fig. 4.
Figs. 5 and 6 show the dynamic response of a two area
reheat thermal system of HNF controller, FLC and
conventional PI controller with and without GRCs for 1% step
load perturbation in area 1. In all the Figs. the response with
conventional PI controller is shown with dotted lines (legend
PI), the response with conventional Fuzzy logic controller is
shown with dashed lines (legend FC) and the response with
proposed HNF controller is shown with solid lines (legend
HNF). It can be seen from Figs. 5 and 6 that even with GRCs
the system remains stable and that the responses with and
without GRCs are not much different. The simulation results
prove that the designed HNF controller is robust in its
operation and gives a superb damping performance both for
frequency and tie line power deviation compare to its
conventional PI as well as FLC counterpart. Besides the
simple architecture of the controller it has the potentiality of
implementation in real time environment.

(4)


E. Steps to design HNF Controller
1.
2.
3.
4.

Load the training data collected in Step.1 and generate the
FIS with gbell MF’s.
Train the collected data with generated FIS upto a
particular no. of Epochs.

Draw the Simulink model with FLC and simulate it with
the given rule base.
The first step to design the HNF controller is collecting
the training data while simulating with FLC.
The two inputs, i.e., ACE and d(ACE)/dt and the output
signal gives the training data.
Use anfisedit to create the HNF .fis file.

Fig. 4. Block diagram of two-area reheat thermal system with HNF controller simulated in Matlab

4


0.02
0.05
0

f1 (pu)

f1 (pu)

0
-0.02

-0.04

-0.06

FC

FC
HNF
0

5

10

15

20

25

-0.1
-0.15

PI

-0.08

-0.05

HNF
PI

-0.2
30

0

Time (sec)

10

20

30

40

50

Tim (sec)

(a) Frequency deviation in Area-1 neglecting GRC

(a) Frequency deviation in Area-1 with GRC

0.05

0

0

-0.01

f2 (pu)

f2 (pu)

0.01

-0.02
-0.03
PI

-0.05

FC
HNF

-0.06

-0.05
-0.1

-0.04

0

5

10

15

20

25

-0.15

FC
HNF

-0.2

30

Time (sec)

PI
0

10

20

30

40

50

Time (sec)

(b) Frequency deviation in Area-2 neglecting GRC

(b) Frequency deviation in Area-2 with GRC

0

0

-0.05
-0.1

-0.2

-0.15

PTIE (pu)

PTIE (pu)


0.1
0.02

-0.2
-0.25
-0.3

-0.6
PI

-0.35

FC

FC

-0.4
-0.45

-0.4

-0.8

HNF
0

10

20

30

40

50

HNF
PI
0

Time (sec)

20

40

60

80

100

Time (sec)

(c) Tie-line power deviation neglecting GRC

(c) Tie-line power deviation with GRC

Fig. 5. Dynamic response of Two-area reheat thermals system
without consideration of generation rate constraint (GRC)

Fig. 6. Dynamic response of Two-area reheat thermals system with
generation rate constraint (GRC)

5


VI. CONCLUSION
In this study, Hybrid Neuro-Fuzzy (HNF) approach is
employed for an Automatic Generation Control (AGC) of
interconnected power system with and without generation rate
constraint (GRC). The effectiveness of the proposed controller
in increasing the damping of local and inter area modes of
oscillation is demonstrated in a two area interconnected power
system. Also the simulation results are compared with a
conventional PI controller. The result shows that the proposed
intelligent controller is having improved dynamic response
and at the same time faster than conventional PI controller. It
has been observed that in the presence of stringent GRC of 3%
min., the performance of HNF controller is almost same to
that of Fuzzy logic Controller (FLC). It can also be observed
that there is a slight improvement in performance of the
system with HNF controller in comparison to FLC in both
cases (i.e. with and without consideration of GRC)

[17]

[18]

[19]

[20]

[21]

[22]

Gayadhar Panda was born in Orissa, India,
in 1970. He received the B.E. degree in
electrical engineering from the Institution of
engineers (I), the M.E. degree in electrical
engineering from Bengal Engineering
College, Shibpur, Howarh, India, and the
Ph.D. degree in electrical engineering from
Utkal University, Orissa, India, in 1996,
1998, and 2007, respectively. Since 1999, he
has been with the Department of Electrical and Electronics
Engineering, Faculty of Engineering, National Institute of Science
and Technology, Berhampur, Orissa, India where he is currently a
Professor. His research interests are in the areas of stability aspects of
power transmission, power electronics, and development of
renewable energy. Dr. G. Panda is a member of the IE and ISTE of
India.


APPENDIX
The nominal system parameters are: f = 60 Hz, Rk = 2.4 Hz / Unit,
Tg = 0.08 Sec, Tr = 10.0 Sec, Hk = 5 .0 Sec, Kr= 0.5, Tf = 0.3 Sec,
2 Tki = 0.05 Mw, Dk= 0.00833 pu Mw/Hz

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Sidhartha Panda is a Professor at National
Institute of Science and Technology (NIST),
Berhampur, Orissa, India. He received the
Ph.D. degree from Indian Institute of
Technology, Roorkee, India in 2008, M.E.
degree in Power Systems Engineering in 2001
and B.E. degree in Electrical Engineering in
1991. Earlier he worked as Associate
Professor in KIIT University, Bhubaneswar,
India and VITAM College of Engineering,
Andhra Pradesh, India and Lecturer in the Department of Electrical
Engineering, SMIT, Orissa, India.His areas of research include power
system transient stability, power system dynamic stability, FACTS,
optimisation techniques, distributed generation and wind energy.
Cemal Ardil is with National Academy of Aviation, AZ1045, Baku,
Azerbaijan, Bina, 25th km, NAA

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