International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 6- Dec 2013
Study of Controller for Economic Load Dispatch by
Generators for Varying Load Demands
Sanjay Kumar Mathur1, G.K. Joshi2
1
2
Research Scholar, Mewar University, Gangrar, Chittorgarh, Rajasthan, India, er_skmathur@yahoo.co.in
Professor & Head Deptt. of Electrical Engg.MBM Engg. College,JN Vyas University, Jodhpur, Rajasthan, India
Abstract—The paper presents a controller for feeding power
to the load on the generator to ensure economic load
dispatch for varying load demands on the generating plant.
What shall be the fuel supply for feeding the desired power
demand on the generator? has been obtained with the help of
a neural network. the experience of operating personals have
been used to provide training data for feed forward network
. The test results of feed forward network as a knowledge
base to set throttle opening i.e. fuel supply to the generator
so that it supplies the desired power demand. The power
delivered by the generator equals the power demand as a
result of the control exercised on fuel supply for economic
load dispatch by the simulink model.
Keywords-Throttle opening, feedback controller, simulink
model, economic load dispatch, knowledge base, feed
forward network, power delivered, varying load demand.
Due to such a control the field current (If )ref. keeps changing
with changing power demand on the generator.
A simulink with an intuitively developed transfer function has
been developed to know the working of a controller is made real
time. The simulink based controller has been given different
values of field currents viz (If ) ref and the power generated is
estimated. The time response for field current (If ) ref = 5.1A is
found that yields power equal to the one provided by the
knowledge base provided by ANN.
The paper shall cover 03 sections. Section I, covers the basic
controller model. Section II deals with development of
knowledge base, using the ANN Approach. Section III : deals
with the development of simulink.
SECTION-I
1.1 INTRODUCTION
2.1 BASIC CONTROLLER MODEL
The paper aims to develop a controller of fuel supply to a
generator so that it supplies the power equal to the power
demand as per the conditions of economic load dispatch. A
generator specific 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 requires more throttle opening
which gives more power generation to meet the increased load
demand and vice- versa.
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.
The flux control approach of speed control of a separately
excited D.C. motor is used to control fuel supply that enabled
the generator to supply power equal to power demand. The field
current decides the opening area of throttle and therefore the fuel
supply and the generator output.
The knowledge base has been developed by using the experience
of operators for a training of feed forward network.
ISSN: 2231-5381
Fuel
Input
N α (V-IaRa)/Ф
Throttle
Control
D.C.
Supply
Alternator
Turbine
3-Ф
A.C.
Output
Ͻ
Ͻ
Speed/
Voltage
Converter
Fig. 1. Control scheme for Fuel supply rate
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
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 6- Dec 2013
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. The idea to
control the fuel supply rate (α) follows the following algorithm.
206.
207.
208.
209.
210.
.
.
.
246.
247.
248.
249.
250.
If load demand (P) is increased
Shutter opens larger
The flux φ goes higher
Fuel supply rate (α) slows down
10.32
10.37
10.42
10.47
10.52
.
.
.
12.45
12.5
12.55
12.6
12.65
204.2
205
205.8
206.6
207.4
.
.
.
238.4
239.2
240
240.8
241.6
Shutter settles to specific size
SECTION-III
4.1 CONTROLLER & ITS SIMULINK
Fuel supply matches with power demand
Fig. 2. 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.
4.1.1 CONTROLLER
The block diagram of controller to ensure that generator delivers
P as desired by economic load dispatch, the control has been
applied on fuel supply (α).
If actual
Shutter
SECTION-II
3.1
TABLE 1: TRAINING DATA FOR ANN BASED ON THE EXPERIENCE OF WORKING
PERSONNEL OF VARIOUS THERMAL STATIONS
Sr.
No.
1.
2.
3.
4.
5.
6.
7.
8.
.
.
.
200.
( If )
0.07
0.12
0.17
0.22
0.27
0.32
0.37
0.42
.
.
.
10.02
Expected power
“P” as provided
by ANN
40.2
41
41.8
42.6
43.4
44.2
45
45.8
.
.
.
199.4
( If )
201.
202.
203.
204.
205.
10.07
10.12
10.17
10.22
10.27
Expected power
“P” as provided
by ANN
200.2
201
201.8
202.6
203.4
ISSN: 2231-5381
Power (P)
Fig. 1. 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 ΔI 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 ΔI=0
and the shutter will be set to new opening and new fuel supply
rate α. This would match with increase in power demand. This
procedure is repeated every time the power demand changes
occures on the controller .
4.1.2 SIMULINK MODEL OF CONTROLLER AND ITS TESTING
TABLE 2: TESTING DATA: AS PROVIDED BY ANN AFTER TRAINING AS IN
TABLE 1
Sr. No.
Generator
Power (P) / If
Converter
DEVELOPMENT OF TRAINING DATA FOR ANN
ANN has been trained to get a knowledge base for field current
(If) for given values of load demand (P).
Turbine
Simulink has been developed with T=0.3 secs. The transfer
function for shutter, turbine and generator has been chosen to be
each. The transfer function for feedback path is taken
1
(0 .33 S  1)
as 1 intutively. The entire transfer function has been multiplied
8
to K. Thus based on empirical relations the transfer function
has been taken as .
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 6- Dec 2013
T.F. =
xlabel (‘Time(secs)’);
K
................(1)
S T1  3S T  3ST1  1.138
3
2
2
1
ylabel(‘Amplitude’);
For T1 = 0.3
T .F . 
A simulink for the controller has been developed which gives
K
.......... ..( 2)
3
0.33 S  0.27TS 2  0.95S  1.138
power output P for specific field current (If) / fuel supply rate (α)
as shown in figure 2
The MATLAB programme for obtaining the step response of the
system is given below
n= [0
0
0
23]
d= [0.33
0.27
0.95
1.138]
step (n,d);
grid on;
title
(‘plot
of
the
unit
step
G(s)=([23]/[0.33s^3+0.27s^2+0.95s+1.138])
response
of
Gain
24/23/22
Fig. 2. Simulink for controller to control power by fuel supply rate (α) /Field current ( If )
The simulink is tested for every value of field current (If) but
only the sample case for If = 2A is given in Fig. 3. It shows
151.5 MW, which is close to the one decided by ANN data base.
Fig. 3. The time response for If =5.1A.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 6- Dec 2013
Table 3 Shows the power output of the generator for given
value of field current (If)
TABLE 3 PERFORMANCE OF CONTROLLER.
1
6.8
Power P
(MW)
Controller
139
2
7.5
150
24
3
8
160
24
4
8.5
169.5
24
5
9
181
24
6
9.5
190
24
7
10.5
202
23
8
11.5
221
23
9
12.5
236
22
10
13
241
22
Sr.
No.
If
Controller
Gain Coefficient (K)
24
REFERENCES
300
250
200
Controller
100
Error
50
0
-50
The challenge is to develop a real time controller to accomplish
economic dispatch.
[1]
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 Fig. 4
150
FUTURE SCOPE
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[2] D.C.Walters, G.B.Sheble. Genetic algorithm solution of economic dispatch
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[3] Park, J.H. ; Kim, Y.S. ; Eom, I.K. ; Lee, K.Y. “Economic load dispatch for
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( If 6.3 7 7.5 8 8.5 9 10 11 12
).
AUTHORS PROFILE
Fig. 4. Error between Controller and ANN
The error between controlling power of controller and ANNKnowledge base is within 5%. This inspires to develop real time
controller.
CONCLUSION
The objective to accomplish the generators to deliver the power
demand by the theory of economic load dispatch has been
obtained by developing a controller which specifies and executes
the fuel supply to agree with power demand. The study is based
on simulink model of a feedback controller. Load affects the
speed and speed affects the throttle opening and therefore fuel
supply rate has been altered with power demand allocated to the
generator.
ISSN: 2231-5381
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.
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
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International Journal of Engineering Trends and Technology (IJETT) – Volume 6 Number 6- Dec 2013
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
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