development of controller for economic load dispatch by generating units und

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –

6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME

159

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.

INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING &

TECHNOLOGY (IJEET)

ISSN 0976 – 6545(Print)

ISSN 0976 – 6553(Online)

Volume 4, Issue 4, July-August (2013), pp. 159-171

© IAEME: www.iaeme.com/ijeet.asp

Journal Impact Factor (2013): 5.5028 (Calculated by GISI)

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160

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.



161

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

a

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

Field Current

If

Vdc

+

-

VTG

Eb

Armature Current

Ra

Nd.c.

Liters/sec Torque

To Load (P)

VTG



162

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

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.

Shutter opens larger

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 smaller

Power supplied by the generator =

power demand on the generator

Shutter settles to specific size

Fuel supply matches with

increased power demand

Fuel supply rate (α) slows down

The generator speed N =synchronous speed Ns

The flux φ goes higher

Speed of DC motor ’Ndc’ goes lower

Fuel supply rate (α) goes higher

Generator Speed (N) goes higher

(VTG) the techogenerator voltage goes higher

The field current (If ) goes higher



163

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

Sr. No. ( If )

Expected power

“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.

No. ( If )

Expected power “P”

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



164

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



165

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



166

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



167

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

)13.0(

1

+S

each. Also the feedback path transfer function is taken as 5.7

1 intutively. The entire transfer

function has been multiplied to gain K. Thus based on empirical relations the transfer function has

been taken as .

T.F. = )1......(..........133.133 1

2

1

2

1

3+++ STTSTS

K

For T1 = 0.3

)2..(..........133.19.02.03.0

..23

+++

=

STSS

KFT

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’);

If ref

Shutter Turbine Generator

Power (P) / If

Converter

Power (P)

If actual

Fuel supply rate (α) Ns

International Journal of Electrical E

6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July

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 7. Simulink for controller to control power by fuel supply

The simulink is tested for every

is given in Fig.e 9

Figure 8.

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976

6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME

168


shown in figure 8

Simulink for controller to control power by fuel supply rate (α) /Field current ( I

The simulink is tested for every value of field current (If) but only the sample case for I

Figure 8. The time response for If =5A

ngineering and Technology (IJEET), ISSN 0976 –

August (2013), © IAEME


) /Field current ( If )

t only the sample case for If = 5A

Gain

24/23/22



169

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

Sr. No. If Power P (MW) Controller Controller Gain Co-

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

Sr. No. ( If )

Power output

suggested by the

ANN

Power output of

generator due to

Controller

Error Controller

Gain

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

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.

-50

0

50

100

150

200

250

300

( If ). 6.3 7 7.5 8 8.5 9 10 11 12

ANN2

Controller

Error



170

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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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.