1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING &
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
TECHNOLOGY (IJEET)
ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 4, Issue 5, September – October (2013), pp. 104-114
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IJEET
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APPLICATION OF HYBRID NEURO FUZZY CONTROLLER FOR
AUTOMATIC GENERATION CONTROL OF THREE AREA
POWER SYSTEM CONSIDERING PARAMETRIC UNCERTAINITIES
CH. Ravi Kumar
Dr. P.V.Ramana Rao
Assistant Professor/E.E.E,
University College of Engg & Tech.
Acharya Nagarjuna University
Guntur - 522 510, India
Professor & H.O.D/E.E.E,
University College of Engg & Tech.
Acharya Nagarjuna University,
Guntur - 522 510, India
ABSTRACT
This paper presents the application of an Adaptive Neuro Fuzzy Inference System (ANFIS)
based intelligent hybrid neuro fuzzy controller for Load Frequency Control of a Three Area Power
System considering parameter uncertainties. The designed controller is found to work satisfactorily
for wide range of variation in parameters up to ±50%, meeting the required specifications. The
dynamic response of the system has been studied for 1% and 10% step load perturbations in area2.
The performance of the proposed Neuro Fuzzy Controller is compared against Fuzzy Integral
controller. Comparative analysis demonstrates that the proposed intelligent Neuro Fuzzy controller is
the most effective of all in improving the transients of frequency deviations against small step load
disturbances. Simulations have been performed using Matlab/Simulink.
Keywords: Automatic Generation Control, Area Control error, Fuzzy Integral Control, Artificial
Neural Networks, ANFIS.
I. INTRODUCTION
Automatic Generation Control or Load Frequency Control is important in Electrical Power
System design and operation. In the event of sudden load perturbation in any area the deviations of
frequencies of all the areas and the tie-line powers occur, which have to be corrected to ensure
generation and distribution of good quality electric power. This is achieved by AGC, the main
objective of which is to keep the system frequency and inter area tie-line power as near to scheduled
values as possible through suitable control action. Many researchers have applied different control
strategies, such as classical control, optimal state feedback control etc. to the AGC problem in order
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2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
to improve performance. They were designed for one operating point only. The model is usually
made of reduced Power System, which includes many generators, turbines and speed governors etc.
Some parameters of the model change depending on the operating condition of Power System.
Controllers which are designed based on a fixed plant model may not work when some system
parameters have been varied. The advent of intelligent control techniques has solved this problem to
a great extent.
Neuro-Fuzzy systems for example have emerged from the fusion of Artificial Neural
Networks (ANN) and Fuzzy Inference Systems (FIS) and form a popular frame work for solving real
world control problems. There are several approaches to integrate ANN and FIS and very often
choice depends on the application. One such important integration is the Adaptive Neuro Fuzzy
Inference System which is presently available in Matlab. In this study an ANFIS based intelligent
hybrid neuro fuzzy controller is proposed as the supplementary controller for AGC of three – area
interconnected system. The dynamic response of the system has been studied for 1% and 10% step
load perturbation in area-2.
A comparison of the proposed controller is made with the Fuzzy Integral controller to show
the relative goodness of the proposed control strategy. The settling times, overshoots and under
shoots of the frequency deviations are taken as performance indices. Comparative analysis shows
that the proposed hybrid neuro fuzzy controller is the most effective of all in improving the transients
of frequency deviations against small step load disturbances.
II. CONFIGURATION OF THREE-AREA POWER SYSTEM
Tie-line
Area
1
Area
2
Area
3
Fig.1 Configuration of Three area Interconnected system
As shown in fig1, the three-area interconnected system is taken as a test system in this study.
The conventional AGC scheme has two control loops: The primary control loop, which controls the
frequency by self-regulating feature of the governor, however, frequency error is not fully
eliminated; and the supplementary control loop, which has a controller that can eliminate the
frequency error with the help of conventional integral action or any suitable controller. The main
objective of supplementary control is to restore balance between each control area load and
generation after a load perturbation so that the system frequency and tie-line power flows are
maintained at their scheduled values. So the control task is to minimize the system frequency
deviations in the three areas and the deviation in the tie-line power flow ∆Ptie between any two areas
under the load disturbances ∆Pd1 or ∆Pd2 or ∆Pd3 in three areas. This is achieved conventionally with
the help of a suitable integral control action. The supplementary controller of the ith area with integral
gain Ki is therefore made to act on ACEi, given by (1), which is an input signal to the controller.
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3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
n
ACEi = ∑ ∆Ptie,ij + Bi ∆f i
(1)
j =1
Where ACEi is area control error of the ith area
∆f i = Frequency error of ith area
∆Ptie,ij = Tie-line power flow error between ith and jth area
Bi = frequency bias coefficient of ith area
II. FUZZY LOGIC CONTROLLERS
The concept of fuzzy logic was developed to address uncertainty and imprecision which
widely exists in engineering problems. Fuzzy logic controllers are rule based
controllers. The design of fuzzy logic controllers involves four stages.
i. Fuzzification ii. Knowledge base iii. Inference engine iv.Defuzzification
Fuzzification: The process of converting a real number into a fuzzy number is called fuzzification.
Knowledge base: This includes, defining the membership functions for each input to the fuzzy
controller and designing necessary rules which specify fuzzy controller output using fuzzy variables.
Inference engine: This is mechanism which simulates human decisions and influences the control
action based on fuzzy logic.
Defuzzification: This is a process which converts fuzzy controller output, fuzzy number, to a real
numerical value.
III. FUZZY INTEGRAL CONTROLLER
This is a combination of Conventional integral controller and Fuzzy controller. For the
proposed controller the mamdani fuzzy inference engine is used and the inference mechanism is
realized by seven triangular membership functions (MFs) for each of the three linguistic variables
(ACEi, dACEi/dt, Ci) with suitable choice of intervals of the MFs as shown in figs 2,3 & 4.
Fig.2 Input Membership Function for ACE
Fig.3 Input Membership Functions for d (ACE)/dt
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4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
Fig.4 Output Membership Functions for Ci
Here ACEi and dACEi/dt act as the inputs of the fuzzy logic controllers and Ci is the output of
fuzzy logic controller. The number of linguistic terms used for each linguistic variable determines
the quality of control which can be achieved using fuzzy logic controller. Generally as the number of
linguistic terms increases, the quality of control improves but this improvement comes at the cost of
increased complexity on account of computational time and memory requirements due to increased
number of rules. Therefore, a compromise between quality of control and complexity involved is
needed to choose the number of linguistic terms, each one of which is represented by membership
function, for each linguistic variable. In this study seven linguistic terms have been chosen for each
of the three variables. The appropriate fuzzy linguistic terms used in this study are given as table 1.
Table 1. Fuzzy Linguistic terms
NB
Negative Big
NM
Negative Medium
NS
Negative Small
ZE
Zero
PS
Positive Small
PM
Positive Medium
PB
Positive big
d/dt(ACE)
Defuzzification has been performed by using bisector of area method. The control rules for
the proposed controller are very simple and have been developed from view point of practical
systems operation and by trial and error methods. The fuzzy rules as used in this study are given in
table 2.
NB
NM
NS
ZE
PS
PM
PB
Table 2. Rule base for Fuzzy Integral Controller
ACE
NB
NM
NS
ZE
PS
PM
NB
NB
NB
NB
NM
NS
NB
NB
NM
NM
NS
ZO
NB
NM
NM
NS
ZO
PS
NM
NM
NS
ZO
PS
PM
NM
NS
ZO
PS
PM
PM
NS
ZO
PS
PM
PM
PB
ZE
PS
PM
PB
PB
PB
107
PB
ZO
PS
PM
PM
PB
PB
PB

5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
Fig.5 shows Simulink Model for Three area Power System with Fuzzy Integral Control
Gain6
20.6
Gai n
1
s
Integrator2
20
Gai n7
0.2
1
Subtract8
0.5s+1
Governor1
Subtract
1
0.2s+1
Fuzzy Logic
Controller
Turbi ne1
1
10s+0.6
Generator1
Subtract1
AREA1
du/dt
Derivative
Gain2
Scope1
1
s
2
Gain8
1
s
Subtract4
Integrator
0.2
AREA2
Integrator3
Scope
1
Fuzzy Logic
Controller2
Subtract9
1
0.3s+1
du/dt
Gain5
Derivative1
1
0.6s+1
Governor2
Subtract2
Turbi ne2
8s+0.9
Subtract3
Gain1
Step1
16
16.9
Gain3
Gain9
1
s
Scope6
Generator2
1
s
2
-K-
AREA3
Integrator1
Subtract6
Integrator4
1
du/dt
Subtract7
Fuzzy Logic
Controller1
1
0.2s+1
Subtract10
0.5s+1
Governor3
Turbine3
1
10s+0.6
Subtract5
Generaor3
Gai n4
Derivati ve2
Gain10
20
20.6
Fig 5. Simulink Model for Three area Power System with Fuzzy Integral Control
IV. THE PROPOSED HYBRID NEURO FUZZY CONTROLLER
In this work an Adaptive network based inference system (ANFIS) is proposed in order to
generate fuzzy membership functions and control rules for the hybrid neuro fuzzy controller. A fuzzy
integral controller is used to provide the required training data. The controller design process consists
of generating input – output data pairs to identify the control variables range and fuzzy membership
functions and then to tune or adapt them using an ANFIS network structure. The controller inputs are
area control error (ACE), and the rate of change of area control error d(ACE)/dt and the output is the
control signal.
Steps to design Hybrid Neuro fuzzy controller:
1. Draw the simulink model of power system under consideration with Fuzzy integral controller and
simulate it with the given rulebase.
2. Collect the training data while simulating with fuzzy integral controller. The two inputs ACE and
d(ACE)/dt and the output signal of the controller form the training data. The training data gives as
much information as possible about the plant behavior for different load perturbations.
3. Use anfisedit to create .fis file.
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6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
4. Load the training data collected in step2 and generate the FIS with suitable (like gaussian/gbell
etc.) membership functions.
5. Train generated FIS with the collected data up to a certain number of epochs.
In this study ANFIS is trained with back propagation algorithm, using ten epochs and step loads of
1% and 10%.
Fig.6 shows Simulink Model for Three area Power System with ANFIS Control
Gain6
20.6
Gain
20
1
1
1
0.2s+1
Subtract8
Subtract
0.5s+1
Governor1
Turbine1
10s+0.6
Generator1
Subtract1
ANFIS
Control ler 1
AREA1
du/dt
Derivative
Gain2
Scope5
1
s
2
Subtract4
Integrator
AREA2
Scope7
ANFIS
Controller 2
Subtract9
1
1
1
8s+0.9
0.3s+1
Subtract2
du/dt
0.6s+1
Governor2
Gain1
Turbine2
Generaor2
Subtract3
Gain5
Derivative1
Step1
16
16.9
Gain3
1
s
2
AREA3
Scope6
Integrator1
1
du/dt
Fuzzy Logic
Controller1
Subtract7
Derivative2
Gai n10
0.5s+1
Governor3
Turbine3
1
1
0.2s+1
Subtract10
Subtract6
10s+0.6
Subtract5
Generator3
Gain4
20
20.6
Fig6. Simulink Model for Three area Power System with ANFIS Control
V. RESULTS AND DISCUSSIONS
In the present work Automatic Generation Control of three area interconnected power system
has been developed using Fuzzy integral controller and ANFIS control to demonstrate the
performance of load frequency control using Matlab/Simulink package. Figs 7 to 14 respectively
represent the plots of change in system frequency for 1% and 10% step load variations considering
parameter variations upto ±50%. Two types of Simulink models are developed with Fuzzy integral
and Hybrid Neuro Fuzzy controllers to obtain better dynamic behavior. The results obtained are also
given in Tables 3 and 4 along with the Parameter variations which are given in Table5.
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7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
Case I: For 1% Step load Perturbation
-4
6
Change in frequency with Fuzzy integral controller
x 10
Del f1
Del f2
Del f3
D iatio in freq cy (p .)
ev
n
uen
.u
4
2
0
-2
-4
-6
0
5
10
15
20
25
30
35
40
45
50
Time in Seconds
Fig7. Frequency deviations ∆f1, ∆f2& ∆f3 with Fuzzy Integral Control
6
x 10
-4
Change in frequency with ANFIS controller
Del f1
Del f2
Del f3
D i to i feunyp .
e a nnr qec ( . )
v i
u
4
2
0
-2
-4
-6
0
5
10
15
20
25
Time in Seconds
30
35
40
45
50
Fig8. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control
-4
Change in frequency with ANFIS control considering +50% Parameter variations
x 10
6
Del f1
Del f2
Del f3
Cag inr qec( . .
hne feuny u
p )
4
2
0
-2
-4
-6
0
5
10
15
20
25
30
35
40
45
50
Time in Seconds
Fig9. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control considering +50% Parameter
variations
-4
6
Change in frequency with ANFIS control considering -50% Parameter variations
x 10
Del f1
Del f2
Del f3
D v tio infr q e c (p .)
e ia n
e u n y .u
4
2
0
-2
-4
-6
0
5
10
15
20
25
30
35
40
45
50
Time in Seconds
Fig10. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control considering -50% Parameter
variations
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8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
Case II: For 10% Step load Perturbation
4
x 10
-3
Change in frequency with Fuzzy Integral Control
Del f1
Del f2
Del f3
C a g infr q e c (p .)
hne
e u n y .u
2
0
-2
-4
-6
-8
0
5
10
15
20
25
Time in Seconds
30
35
40
45
50
Fig11. Frequency deviations ∆f1, ∆f2& ∆f3 with Fuzzy Integral Control
Change in frequency with ANFIS Control
-3
2
x 10
Del f1
Del f2
Del f3
C a g inf e u n y( . .
h n e r q e c pu)
0
-2
-4
-6
-8
-10
0
5
10
15
20
25
30
35
40
45
50
Time in Seconds
Fig12. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control
-3
2
Change in frequency with ANFIS control considering +50% parameter variations
x 10
Del f1
Del f2
Del f3
D v tio infre u n yp .)
e ia n
q e c ( .u
0
-2
-4
-6
-8
-10
0
5
10
15
20
25
30
35
40
45
50
Time in Seconds
Fig13. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control considering +50% Parameter
variations
Change in frequency with ANFIS control considering (-50%) Parameter variations
-3
4
x 10
Del f1
Del f2
Del f3
D via n in F u n
e tio
rq e cy(p .)
.u
2
0
-2
-4
-6
-8
-10
0
5
10
15
20
25
30
35
40
45
50
Time in Seconds
Fig14. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control considering -50% Parameter
variations
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9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
VI. CONCLUSIONS
Table 3: Comparative study of Settling time and Peak overshoots for 1% step load variation
Controllers
Fuzzy Integral
ANFIS
ANFIS for +50%
Parameter variations
ANFIS for -50%
Parameter variations
Settling time in (Sec)
∆f
∆f
Area 1
Area 2
∆f
Area 3
Peak overshoot (p.u.) X 10-4
∆f
∆f
∆f
Area 1
Area 2
Area 3
15
10
25
15
15
10
0.25
-1
4
5
0.25
-1
20
20
20
-1.5
5
-1.5
10
10
10
-1
5
-1
Table 4: Comparative study of Settling time and Peak overshoots for 10% step load variation
Controllers
Fuzzy Integral
ANFIS
ANFIS for +50%
Parameter variations
ANFIS for -50%
Parameter variations
Settling time in (Sec)
∆f
∆f
Area 1
Area 2
∆f
Area 3
Peak overshoot (p.u.) X 10-3
∆f
∆f
∆f
Area 1
Area 2
Area 3
20
15
25
15
20
15
-1.5
-1.5
-8
-8
-1.5
-1.5
15
15
15
-1.8
-8
-1.8
12
12
12
-1.5
-8
-1.5
Table 5: Parameter variations
Nominal value
Parameter
Governor Time Constant
(Seconds)
Turbine Time Constant
(Seconds)
Generator Time Constant
(Seconds)
Variations considered
Areas 1 & 3
Area2
Areas 1 & 3
Area2
0.2
0.3
0.1 – 0.3
0.15 - 0.45
0.5
0.6
0.25 - 0.75
0.3 – 0.9
5
4
2.5 – 7.5
2-6
In this study, Hybrid Neuro Fuzzy approach is employed for an Automatic Generation
Control (AGC) system. The proposed controller can handle the non linearity’s and parametric
uncertainties and at the same time is faster than the Fuzzy integral controller. The effectiveness of
the proposed controller in increasing the damping of local inter area modes of oscillation are
demonstrated using a three area interconnected power system. Also the simulation results are
compared with Fuzzy integral controller. The results show that the proposed ANFIS controller is
having improved dynamic response and at the same time faster than Fuzzy integral controller.
From the above tables, the responses obtained reveal that ANFIS controller has better settling
performance than Fuzzy integral controller. Therefore Intelligent control approach using ANFIS is
more accurate and faster than fuzzy integral control scheme even for complex and dynamic systems,
with parametric variations.
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10. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
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11. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 4, Issue 5, September – October (2013), © IAEME
BIOGRAPHY
Ch.Ravi Kumar was born in India in 1981; He received the B.Tech degree in
Electrical and Electronics Engineering from A.S.R.College of Engineering and
Technology, Tanuku in 2003 and M.Tech degree from JNTU Anantapur, A.P.-India
in 2005. Currently he is pursuing Ph.D in Electrical Engineering and working as
Asst.Professor in University college of Engineering and Technology, Acharya
Nagarjuna University, Andhra Pradesh India. His areas of Interest are Power system
operation and control, Application of Intelligent control techniques to Power systems.
P.V.Ramana Rao was born in India in 1946; He received the B.Tech degree in
Electrical and Electronics Engineering from IIT Madras, India in 1967 and M.Tech
degree from IIT Kharagpur, India in 1969. He received Ph.D from R.E.C Warangal in
1980. Total teaching experience 41 years at NIT Warangal out of which 12 years as
Professor of Electrical Department. Currently Professor of Electrical Department in
University college of Engineering and Technology, Acharya Nagarjuna University,
Andhra Pradesh, India. His fields of interests are Power system operation and control, Power System
Stability, HVDC and FACTS, PowerSystem Protection, Application of DSP techniques and Applicat
ion of Intelligent control techniques to Power systems.
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