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					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)                                                       IJEET
Volume 4, Issue 5, September – October (2013), pp. 104-114
© IAEME: www.iaeme.com/ijeet.asp
Journal Impact Factor (2013): 5.5028 (Calculated by GISI)                   ©IAEME
www.jifactor.com




      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,                        Professor & H.O.D/E.E.E,
        University College of Engg & Tech.              University College of Engg & Tech.
         Acharya Nagarjuna University                     Acharya Nagarjuna University,
           Guntur - 522 510, India                            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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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                  Area                  Area
                            1                     2                     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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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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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

         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.

                               Table 2. Rule base for Fuzzy Integral Controller
                                                          ACE
                                NB      NM        NS       ZE        PS      PM    PB
                          NB    NB       NB       NB      NB        NM       NS    ZO
                          NM    NB       NB      NM       NM        NS       ZO    PS
              d/dt(ACE)




                          NS    NB      NM       NM        NS       ZO       PS    PM
                          ZE    NM      NM        NS      ZO         PS      PM    PM
                          PS    NM       NS       ZO       PS       PM       PM    PB
                          PM    NS       ZO       PS      PM        PM       PB    PB
                          PB    ZE       PS       PM       PB       PB       PB    PB

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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
                                                                                                       20
                                                             s                 Gai n7
                                                        Integrator2
                                                                               0.2


                                                                                                                  1                 1
                                                                                                                                                                      1
                                              Fuzzy Logic                                                       0.2s+1          0.5s+1
                                               Controller                                   Subtract                                                             10s+0.6
              Subtract8                                                                                   Governor1         Turbi ne1
                                                                                                                                          Subtract1            Generator1


                                 du/dt                                                                    AREA1

                               Derivative
                                                                                                                                                 Gain2
                                                               Scope1
                                                                                                                                                                 1
                                                                                                                                                     2
                                                                    Gain8                                                                                        s
                                                           1                                                                                                 Integrator   Subtract4
                                                                    0.2                                   AREA2
                                                           s
                                                     Integrator3
                                                                                                                                                                                          Scope

                                                                                                            1                   1
                                                    Fuzzy Logic                                                                                                               1
                                                    Controller2                                        0.3s+1              0.6s+1
               Subtract9                                                                                                                                                    8s+0.9
                                                                                     Subtract2      Governor2             Turbi ne2
                                                                                                                                                          Subtract3       Generator2
                                du/dt                                                    Gain1
                                        Gain5
                              Derivative1                                                   16                                           Step1
                                            16.9
                                                                                                                                                 Gain3

                                                                   Gain9                                                                                              1
                                                                                                                                                      2
                                                                                                                                                                      s
                                                       1
     Scope6                                                        -K-                                    AREA3                                                Integrator1    Subtract6
                                                       s
                                                   Integrator4
                                                                                                                                                                  1
                                                                                                            1               1
              Subtract10                                                                               0.2s+1             0.5s+1                              10s+0.6
                                                                                                                                         Subtract5           Generaor3
                                            Fuzzy Logic                                 Subtract7   Governor3            Turbine3
                              du/dt         Controller1                                          Gai n4
                           Derivati ve2
                                                                                                    20
                                                           Gain10

                                                                 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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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              0.5s+1
                  Subtract8                                                                                                                                 10s+0.6
                                                                   Subtract          Governor1            Turbine1
                                                  ANFIS                                                                         Subtract1                  Generator1
                                                Control ler 1                                  AREA1
                                  du/dt

                               Derivative
                                                                                                                              Gain2
                                                                              Scope5                                                               1
                                                                                                                                 2
                                                                                                                                                   s
                                                                                                                                             Integrator              Subtract4
                                                                                               AREA2


                                                                                                                                                                                          Scope7
                                                 ANFIS
                                                                                      1                    1                                                           1
                                               Controller 2
                    Subtract9                                                                                                                                        8s+0.9
                                                                                    0.3s+1               0.6s+1
                                                                 Subtract2                              Turbine2                                                 Generaor2
                                                                                   Governor2
                                                                                                                                       Subtract3
                                   du/dt                                           Gain1
                                              Gain5
                                Derivative1                                          16                               Step1
                                                16.9
                                                                                                                                Gain3
                                                                                                                                                       1
                                                                                                                                      2
                                                                                                                                                       s
       Scope6                                                                                  AREA3                                          Integrator1                     Subtract6

                                                                                                                                                             1
                                                                                               1                      1
                Subtract10                                                                                                                                 10s+0.6
                                                                                           0.2s+1                   0.5s+1
                                                                                                                                      Subtract5        Generator3
                                               Fuzzy Logic             Subtract7          Governor3                Turbine3
                                du/dt          Controller1
                                                                                          Gain4
                             Derivative2
                                                                                               20
                                                      Gai n10

                                                         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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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
                                                                                       x 10                                 Change in frequency with Fuzzy integral controller
                                                                                  6
                                                                                                                                                                                                  Del f1
                                                                                                                                                                                                  Del f2
                                                                                  4                                                                                                               Del f3



                                                  D iatio in freq cy (p .)
                                                                 uen   .u         2



                                                                                  0
                                                         n




                                                                                  -2
                                                   ev




                                                                                  -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
                                                                                            -4
                                                                                  x 10                                           Change in frequency with ANFIS controller
                                                                             6
                                                                                                                                                                                                  Del f1
                                                                                                                                                                                                  Del f2
                                                                                                                                                                                                  Del f3
                                                                             4
             e a nnr qec ( . )
                            u
             D i to i feunyp .




                                                                             2




                                                                             0
              v i




                                                                     -2




                                                                     -4




                                                                     -6
                                                                                  0                  5           10         15           20          25         30           35   40         45              50
                                                                                                                                              Time in Seconds




                                                                                           Fig8. Frequency deviations ∆f1, ∆f2& ∆f3 with ANFIS Control
                                                                                           -4
                                                                                  x 10                       Change in frequency with ANFIS control considering +50% Parameter variations
                                                                             6
                                                                                                                                                                                                    Del f1
                                                                                                                                                                                                    Del f2
                                                                                                                                                                                                    Del f3

                                                                             4
                            p )




                                                                             2
                  hne feuny u
                 Cag inr qec( . .




                                                                             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
                                                                                  x 10                       Change in frequency with ANFIS control considering -50% Parameter variations
                                                                             6
                                                                                                                                                                                                    Del f1
                                                                                                                                                                                                    Del f2
                                                                                                                                                                                                    Del f3
                                                                             4
                       D v tio infr q e c (p .)
                                   e u n y .u




                                                                             2



                                                                             0
                        e ia n




                                                                             -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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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
                                                                    -3
                                                             x 10                            Change in frequency with Fuzzy Integral Control
                                                        4
                                                                                                                                                                   Del f1
                                                                                                                                                                   Del f2
                                                        2                                                                                                          Del f3

                              C a g infr q e c (p .)
                                        e u n y .u
                                                        0



                                                        -2
                               hne




                                                        -4



                                                        -6



                                                        -8
                                                             0           5       10         15          20          25         30         35       40         45            50
                                                                                                             Time in Seconds


                                                 Fig11. Frequency deviations ∆f1, ∆f2& ∆f3 with Fuzzy Integral Control

                                                             x 10
                                                                 -3
                                                                                                 Change in frequency with ANFIS Control
                                                        2
                                                                                                                                                                    Del f1
                                                                                                                                                                    Del f2
                                                                                                                                                                    Del f3
                                                        0
              h n e r q e c pu)
             C a g inf e u n y( . .




                                                        -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
                                                             x 10            Change in frequency with ANFIS control considering +50% parameter variations
                                                        2
                                                                                                                                                                    Del f1
                                                                                                                                                                    Del f2
                                                                                                                                                                    Del f3
                                                        0
             D v tio infre u n yp .)
                          q e c ( .u




                                                        -2



                                                        -4
              e ia n




                                                        -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

                                                             x 10
                                                                 -3
                                                                             Change in frequency with ANFIS control considering (-50%) Parameter variations
                                                        4
                                                                                                                                                                    Del f1
                                                                                                                                                                    Del f2
                                                        2                                                                                                           Del f3


                                                        0
                             rq e cy(p .)
                 D via n in F u n     .u




                                                        -2


                                                        -4
                  e tio




                                                        -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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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
                             Settling time in (Sec)                  Peak overshoot (p.u.) X 10-4
     Controllers           ∆f              ∆f             ∆f             ∆f            ∆f          ∆f
                          Area 1         Area 2          Area 3        Area 1       Area 2        Area 3
Fuzzy Integral              15             25             15            0.25              4           0.25
ANFIS                       10             15             10             -1               5            -1
ANFIS for +50%              20             20             20            -1.5              5           -1.5
Parameter variations
ANFIS for -50%              10             10             10             -1               5             -1
Parameter variations


   Table 4: Comparative study of Settling time and Peak overshoots for 10% step load variation
                             Settling time in (Sec)                  Peak overshoot (p.u.) X 10-3
     Controllers           ∆f               ∆f            ∆f             ∆f            ∆f          ∆f
                          Area 1         Area 2          Area 3        Area 1       Area 2        Area 3
Fuzzy Integral              20             25             20            -1.5              -8           -1.5
ANFIS                       15             15             15            -1.5              -8           -1.5
ANFIS for +50%
                            15             15             15            -1.8              -8           -1.8
Parameter variations
ANFIS for -50%
                            12             12             12
Parameter variations                                                    -1.5              -8           -1.5


                                    Table 5: Parameter variations
                                               Nominal value                   Variations considered
             Parameter               Areas 1 & 3           Area2          Areas 1 & 3            Area2
      Governor Time Constant
                                         0.2                   0.3            0.1 – 0.3        0.15 - 0.45
             (Seconds)
       Turbine Time Constant
                                         0.5                   0.6         0.25 - 0.75          0.3 – 0.9
             (Seconds)
      Generator Time Constant
                                          5                    4              2.5 – 7.5           2-6
             (Seconds)


       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.

                                                   112
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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 [16]   R. Arivoli and Dr. I. A. Chidambaram, “Multi-Objective Particle Swarm Optimization Based
        Load-Frequency Control of a Two-Area Power System with Smes Inter Connected using
        Ac-Dc Tie-Lines”, International Journal of Electrical Engineering & Technology (IJEET),
        Volume 3, Issue 1, 2012, pp. 1 - 20, ISSN Print : 0976-6545, ISSN Online: 0976-6553.
 [17]   J.Srinu Naick and Dr. K. Chandra Sekar, “Application of Genetic Algorithm and Neuro
        Fuzzy Control Techniques for Automatic Generation Control of Interconnected Power
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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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