DEVELOPMENT OF CONTROLLER FOR ECONOMIC LOAD DISPATCH BY GENERATING UNITS UND by iaemedu

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									International Journal of Electrical Engineering and
                                             ELECTRICAL (2013), © IAEME0976 –
INTERNATIONAL JOURNAL OF4, Issue 4, Technology ENGINEERING &
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                                                                (IJEET), ISSN

                             TECHNOLOGY (IJEET)

ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)                                                        IJEET
Volume 4, Issue 4, July-August (2013), pp. 159-171
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  DEVELOPMENT OF CONTROLLER FOR ECONOMIC LOAD DISPATCH
     BY GENERATING UNITS UNDER VARYING LOAD DEMANDS

                                           Sanjay Mathur
              Ph.D Scholar, Mewar University, Gangrar, Chittorgarh, Rajasthan, India

                                          Shyam K. Joshi
                             Ph.D Scholar, IIT Delhi, New Delhi, India

                                             G.K. Joshi
      Professor,& Head Deptt. of Electrical Engg., MBM Engg. College, JN Vyas University,
                                    Jodhpur, Rajasthan, India



ABSTRACT

The paper presents a simulink model of controller for feeding power to the load by the generator, in a
group of generators according to power demand imposed by the conditions of economic load
dispatch on the generating plant. The knowledge base that correlates throttle opening of governor
with specific power demand has been derived using experience based training of a feed forward
network and the same has been used to operate a proposed feedback controller. The controller
ensure that the power delivered by the generator equals the power demand on a specific generator for
a given load state, while maintaining economic load dispatch. The simulink model of the feedback
controller shows that the power delivered by a generator operating in parallel with other generators is
same as the one provided by ANN trained modal. The work can be extended for developing a real
time controller that enables the generator to supply power equal to power demand determined by the
conditions of economic load dispatch.

Keywords: Feedback Controller, Simulink, Economic Load Dispatch, Feed Forward Network,
Knowledge Base.




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1.1 INTRODUCTION

         The Aim of the present work is to develop a controller, which can manage the requisite
amount of fuel supply to a generator so that it supplies the power equal to the power demand
developing upon the generator as a result of conditions of economic load dispatch. A dedicated fuel
supply control is needed for each generator among the group of generators in the plant. For this
purpose the throttle valve / shutter of the governor is coupled with the shaft of turbine feeding
mechanical power to the generator rotor. Higher the load demand larger would be the throttle
/shutter opening and higher would be the fuel supply leading to more power generation to meet the
increased load demand and vice- versa.
         The concept of flux control of speed of a separately excited D.C. motor has been used to
control fuel supply that enabled power supply equal to power demand. It is therefore certain that a
specific power demand can be supplied if the field current (If) of the D.C. shunt motor is of specific
value. This is because the field current decides the size to which the throttle should open and
therefore the fuel supply that should be given to the generator.
         The knowledge that correlates the specific power demand to the size of field current (If) has
been developed by using the experience of operators. Also the data base of this kind has been
developed using the Artificial neural network working on feed forward network approach. Having
developed the knowledge-base a feedback controller has been developed, where the field current (If
)ref. keeps changing with changing values of power demand on the generator.
         In order to develop a real time controller the developed feedback controller has been
converted into a simulink with an intuitively developed transfer function. The simulink based
controller has been given different values of field currents viz (If ) ref and the corresponding power
generated has been estimated. It is found that the power generated agrees with the power demands,
supplying of which could be made possible by using specific field current (If) ref to be given to the
field of a separately excited D.C. motor for control of throttle opening. The time response for field
current (If ) ref = 5A has been plotted that yields power equal to the one provided by the knowledge
base due to ANN.
         The paper has been organized in 04 sections. Section I, covers the basic controller model.
Section II deals with development of knowledge base, for setting fuel rate supply for specific power
demand: The ANN Approach. Section III deals with feed back controller model for supplying
specific power demand by setting the specific value of field current (If) ref. Section IV : covers the
development of simulink for establishing the control action for feeding specific power demand.


                                            SECTION-I

2.1 BASIC CONTROLLER MODEL
        The basic controller model is given in figure1. It controls the fuel supply rate (α) to ensure
that the generator delivers a specific power demand ‘P’ & helps in maintaining economic load
dispatch.




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                     Liters/sec                                 Torque


                    Nd.c.                                                                To Load (P)




                                        VTG




           Figure 1. (a). Control scheme for Fuel supply rate to meet given load demand



                                  Armature Current
                                                                         Field Current
                            +                                                 If
                            Vdc                                                    VTG
                                                     Ra   a
                                           Eb

                            -

                                         Ndc


                Figure 1. (b) Shutter speed control by separately excited D.C. Motor


        For this purpose it is necessary to know the size of field current (If ) for enabling the
generator to deliver given power (P) for every value of load demand. i.e. what would be (If ) for
given (P). This knowledge has been obtained by training the ANN with the physical values of power
(P) and the field current (If ) that gives this power. This is because the field current gives the opening
speed of shutter Ndc and therefore the fuel supply rate (α) which in turn decides the power (P) to be
generated. How does the load demand (P) affect the fuel supply rate (α) through the field current (If )
of d.c. motor is given as under




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The idea to control the fuel supply rate (α) follows the following algorithm.

                                            If load demand (P) is increased

                                          The speed (N) of generator goes low


                                                  (VTG ) goes low


                                             (If ) of the motor goes low

                                              Flux (φ) of the motor goes low


                                              The speed (Ndc) goes higher

                                                Shutter opens larger


                                          Fuel supply rate (α) goes higher


                                         Generator Speed (N) goes higher

                                    (VTG) the techogenerator voltage goes higher

                                         The field current (If ) goes higher

                                                 The flux φ goes higher

                                       Speed of DC motor ’Ndc’ goes lower


                                               Shutter opens smaller


                                          Fuel supply rate (α) slows down


                                   The generator speed N =synchronous speed Ns


                                       Power supplied by the generator =
                                        power demand on the generator

                                         Shutter settles to specific size


                                            Fuel supply matches with
                                            increased power demand


             Figure 2. Control strategy for fuel supply rate( α) as per load demand (P)


        Thus the controller works to adjust the fuel supply rate (α) in correspondence with the
specific power demand (P) as determined by the conditions of economic load dispatch for every state
of load demand.




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                                SECTION-II
3.1   KNOWLEDGE BASE TO TRAIN ANN FOR FUEL SUPPLY RATE (α) THAT GIVES SPECIFIC
POWER GENERATION ‘P’

ANN has been trained to get a knowledge base for field current (If) for given values of load demand
(P). The data base for fuel supply rate (α) to generate power (P) has been obtained by the experience
of working personnel from various thermal power stations.

 Table 1: Training data for ANN based on the experience of working personnel of various thermal
                                            stations

                                                           Expected power
                            Sr. No.        ( If )         “P” as provided by
                                                                 ANN
                               1.          0.05                    40
                               2.          0.1                    40.8
                               3.          0.15                   41.6
                               4.          0.2                    42.4
                               5.          0.25                   43.2
                               6.          0.3                     44
                               7.          0.35                   44.8
                               8.          0.4                    45.6
                               .             .                      .
                               .             .                      .
                               .             .                      .
                              200.          10                   199.2


               Table 2: Testing data: as provided by ANN after training as in Table 1

                             Sr.                     Expected power “P”
                                       ( If )
                            No.                      as provided by ANN
                            201.      10.05                  200
                            202.      10.1                  200.8
                            203.      10.15                 201.6
                            204.      10.2                  202.4
                            205.      10.25                 203.2
                            206.      10.3                   204
                            207.      10.35                 204.8
                            208.      10.4                  205.6
                            209.      10.45                 206.4
                            210.      10.5                  207.2
                            211.      10.55                  208
                            212.      10.6                  208.8
                            213.      10.65                 209.6
                            214.      10.7                  210.4
                            215.      10.75                 211.2
                            216.      10.8                   212

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                            217.      10.85              212.8
                            218.      10.9               213.6
                            219.      10.95              214.4
                            220.       11                215.2
                            221.      11.05               216
                            222.      11.1               216.8
                            223.      11.15              217.6
                            224.      11.2               218.4
                            225.      11.25              219.2
                            226.      11.3                220
                            227.      11.35              220.8
                            228.      11.4               221.6
                            229.      11.45              222.4
                            230.      11.5               223.2
                            231.      11.55               224
                            232.      11.6               224.8
                            233.      11.65              225.6
                            234.      11.7               226.4
                            235.      11.75              227.2
                            236.      11.8                228
                            237.      11.85              228.8
                            238.      11.9               229.6
                            239.      11.95              230.4
                            240.       12                231.2
                            241.      12.05               232
                            242.      12.1               232.8
                            243.      12.15              233.6
                            244.      12.2               234.4
                            245.      12.25              235.2
                            246.      12.3                236
                            247.      12.35              236.8
                            248.      12.4               237.6
                            249.      12.45              238.4
                            250.      12.5               239.2
                            251.      12.55               240
                            252.      12.6               240.8
                            253.      12.65              241.6


3.2   DEVELOPMENT OF ANN PLATFORM FOR OBTAINING KNOWLEDGE BASE

3.2.1 Authentication of ANN standards
         In order that the ANN formulated on MATLAB works with high degree of confidence it is
checked for its ability of performance, training states, Regression. A normal feed forward network &
its features in the ANN training window are shown in figure 3




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                                 Figure 2. ANN Training Window

The performance of success of ANN is given in figure 4.




                                  Figure 3. Performance of ANN

        The error regarding training, testing and validation converges to its best values which shows
the authoritative confidence in using ANN for certain test results after proper training.
The training states are given in figure 5



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                                Figure 4. Training States of ANN

The regression is shown in figure 6




                                      Figure 5. Regression of ANN

                                            SECTION-III

4.1   DEVELOPMENT OF FEEDBACK CONTROLLER FOR FUEL SUPPLY RATE ( α )

                           THAT GIVES SPECIFIC POWER GENERATION ‘P’

In order to develop a controller that enables a generator working among a group of generators to
deliver specific power ‘P’, the fuel supply rate (α) has been controlled by controlling the size of
opening of throttle/shutter of Governor. For this purpose the controller has been given the knowledge
base as developed by ANN. The proposed Feedback controller is given in Figure 7

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                                                 Fuel supply rate (α)               Ns
               If ref
                                                                                                        Power (P)
                                          Shutter                  Turbine                  Generator


                        If actual
                                                                   Power (P) / If
                                                                    Converter

       Figure 6. Block diagram of Controller to Power by Fuel supply rate (α) /Field current ( If )

        For every new state of load the new power demand is thrown on the output of a generator. It
therefore required new (If ) ref to be set at the input of the controller. This follows the knowledgw
base given by ANN. If due to change in load state Pdemand becomes higher. It is therefore if P demand is
greater than P demand previous than (If)ref shall be greater than (If)ref previous causing ∆I to be larger and the
shutter will open with larger area leading to a higher rate of fuel supply rate               and therefore more
power output G. When the power demand is supplied fully the ∆I=0 and the shutter will be set to
new opening and new fuel supply rate α. This would match with increase power demand. This
procedure is repeat every time the power demand changes occures on the controller .

                                                            SECTION-IV

5.1 SIMULINK MODEL OF CONTROLLER AND ITS TESTING

        While developing the simulink for controller the transfer function has been developed by
taking T = 0.3 secs. With the justification that despite all the non- linerities the controller operates in
the linear zone. The transfer function for shutter, turbine and generator has been chosen to be
    1    each. Also the feedback path transfer function is taken as 1 intutively. The entire transfer
(0.3 S + 1)                                                                                      7.5
function has been multiplied to gain K. Thus based on empirical relations the transfer function has
been taken as .

                                      T.F. =                      K
                                                                                  ................(1)
                                                   S 3T1 + 3S 2T12 + 3ST1 + 1.133

For T1 = 0.3

                                                              K
                                      T .F . =                                  ............(2)
                                                        3
                                                 0.3 S + 0.2TS 2 + 0.9S + 1.133

The MATLAB programme for obtaining the step response of the system is given below

n= [0 0           0             23]
d= [0.3 0.27 0.9          1.133
step (n,d);
grid on;
title (‘plot of the unit step response of G(s)=([23]/[0.3s^3+0.27s^2+0.9s+1.133])
xlabel (‘Time(secs)’);
ylabel(‘Amplitude’);


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A simulink for the controller has been developed which gives power output P for specific field
current (If) / fuel supply rate (α) as shown in figure 8




                                                                                             Gain
                                                                                            24/23/22




    Figure 7. Simulink for controller to control power by fuel supply rate (α) /Field current ( If )
                                                                             )


                                                                       t
        The simulink is tested for every value of field current (If) but only the sample case for If = 5A
is given in Fig.e 9




                               Figure 8. The time response for If =5A



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Table 3 Shows the power output of the generator for given value of field current (If)

   Table 3 The field current (If) & Power output (P) of Generator as a result of controller operation
                                                                                Controller Gain Co-
      Sr. No.               If             Power P (MW) Controller
                                                                                    efficient (K)
         1                 6.3                          138                               24
         2                  7                           149                               24
         3                 7.5                          159                               24
         4                  8                         168.5                               24
         5                 8.5                          180                               24
         6                  9                           189                               24
         7                 10                           201                               23
         8                 11                           220                               23
         9                 12                           235                               22
        10                12.5                          240                               22

       It is found that as the field current (If) is increased the value of K needs to be reduced so that
the controller delivers the desired response as suggested by the knowledge base of ANN. Training &
Testing. The Error between execution of controller and one suggested by ANN is shown in Table 4

          Table 4: Error between Execution of Controller and one Suggested by ANN
                                Power output         Power output of
                                                                                     Controller
       Sr. No.     ( If )      suggested by the      generator due to       Error
                                                                                       Gain
                                    ANN                 Controller
           1       6.3              140.1                  138               2.1         24
           2        7                152                   149                 3         24
           3       7.5              159.2                  159               0.2         24
           4        8               167.2                 168.5              -1.3        24
           5       8.5               175                   180                -5         24
           6        9                183                   189                -6         24
           7        10              198.2                  201               -2.8        23
           8        11              215.2                  220               -4.8        23
           9        12              231.2                  235               -4.8        22
          10       12.5            239.25                  240              -0.75        22

The error has been plotted in Figure 10

                              300
                              250
                              200
                                                                        ANN2
                              150
                              100                                       Controller
                               50                                       Error
                                0
                               -50 ( If ). 6.3 7 7.5 8 8.5 9 10 11 12

                            Figure 9. Plot of error between Controller and ANN

       The error between controlling power of controller and ANN-Knowledge base is within 5%.
Hence the design can be extended for developing real time controller.

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CONCLUSION

        In order that the generator delivers specific power demand (P), the generator needs to be
fueled with specific fuel supply rate (α). It therefore needs to develop a controller which does this
work. For the purpose even if the controller is developed it cannot work unless the proper knowledge
base is developed for opening of throttle providing fuel supply rate (α) that gives the desired power
generation (P). The present work has contributed the development of (a) A knowledge base for
operating a controller (b) Basic model of controller (c) Feedback controller and (d) A simulink
model of controller.
        This has been found that the throttle opening as decided by the field current (If) for separately
excited D.C. motor enables, the generator to generate power ( P) as per load demand posed on the
generator to implement economic load dispatch for every state of load. The results for step response
of the simulink model of controller has been found for varying values of field current (If). However
the values of increasing If requires the value of K to reduce. The error between controller output and
ANN – Knowledge base has been found to be within 5%.

FUTURE SCOPE

      It is possible to extend the work for developing a real time controller for implementating
economic dispatch.

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 [12] J. Kumar Jayant, and Gerald B. Sheblé, "Clamped State Solution of Artificial Neural Network
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AUTHORS PROFILE

               Sanjay Mathur did his B.E. in Electrical Engineering from Amravati University in
               1998 and M.E. from M.B.M Engg. College Jodhpur. He has worked as Asstt. Prof in
               the Deptt. of electrical Engg at M.E.C.R.C., Jodhpur, Rajasthan, India then worked as
               associate professor at Techno India NJR Institute of Technology, Udaipur. Currently
               he is Ph.D scholar at Mewar University, Gangrar, Chittorgarh, Rajasthan, India. His
area of interests are Circuit Analysis, Economic Operation of Generators, Artificial Intelligence,
Programming languages and Electrical Machines. He has authored a book titled “Concepts of C”. He
is also technical consultant of Techlab Instruments.

               Shyam K Joshi is currently a pursuing Ph.D from Deptt of Electrical Engg, IIT
              Dehi He has obtained M.E (Hons.) in Electrical Engg. with specialization in Control
              Systems & B.E . (Hons) in Electronics & Communication Engg. Game Theory,
              Biological Neural Network , Networked Dynamical Systems, happens to be his ares
              of research interest. Till date he has around 12 publications in various International
Journals , International conferences and Seminars. He is Member of International Association of
Computer Science & Information Technology – Singapore.

               G K Joshi did his B.E., M.E. and Ph.D. in Electrical Engineering from M.B.M.
                Engineering college Jodhpur, Jai Narayan Vyas University, Jodhpur. He has worked
                till now as a lecturer, Sr. lecturer, reader, professor and Principal of Engineering
                College I.E.T. Alwar. Presently he is head deptt. Of electrical engineering MBM
                Engineering college JNVU Jodhpur. He has guided 03 Ph.D, 23 M.E. dissertations, 30
M.E. seminars, 50 technical papers in national, international conferences and journals. Prof. Joshi is
a technical paper reviewer of Institution of Engineers (I). He is a member editorial board of IJCEE,
International Journal for Computer & Electrical Engineering. He is a fellow of Institution of
Engineers (I). He is a life member of ISTE. He has completed many projects under U.G.C. and
AICTE grants and established a high voltage lab of 400KV standard with non-destructive testing
facilities. His area of research is residual life estimation of dielectrics, applications of soft computing
viz. fuzzy, neuro, GA, evolutionary algorithm to practical problems. His subjects of interest are high
voltage engineering, pattern recognition, instrumentation, power systems and electrical machines. He
is presently guiding 6 Ph.D scholars and 4 M.E. students dissertations. He has organized many
international conferences and has been a key note speaker in several international conferences. His
keynote address on estimation of residual useful life of dielectrics using partial discharges” was rated
excellent in the International conference on signal Acquisition and Processing (ICSAP-2011) held at
Singapore on 26-28 Feb. 2011.
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