load shedding design for an industrial cogeneration system

7/28/2019 LOAD SHEDDING DESIGN FOR AN INDUSTRIAL COGENERATION SYSTEM

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Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013

35

LOAD SHEDDING DESIGN FOR AN INDUSTRIAL

COGENERATION S YSTEM

Mukesh Kumar Kirar1

, Renuka Kamdar2

, Manoj Kumar3

, Ganga Agihotri4

1Department of Electrical Engineering, MANIT, Bhopal, India

[email protected], [email protected],

[email protected], [email protected]

A BSTRACT

This paper presents transient stability analysis and enhancement of Industrial Cogeneration Plant (ICP)

using Artificial Neural Network (ANN) based adaptive load shedding. By selecting the total in-plant

generation, spinning reserve, total plant load and rate of change of frequency as the input neurons of the

ANN, the minimum amount of load shedding is determined to maintain in-plant load-generation

equilibrium and to ensure continuity of power supply to critical loads of the plant. The comparison of under

frequency relay based load shedding and ANN based adaptive load shedding is also preformed to evaluate

effectiveness of the ANN based Load Shedding. The system frequency response for deferent generation-load scenarios is also determined. The industrial cogeneration is simulated on ETAP software and transient

stability is analyzed by considering various contingencies and load-generation scenarios. ANN has been

implemented on MATLAB.

K EYWORDS

Artificial Neural Network, Islanded System, Load Shedding, Electrical Transient Analyser Program

(ETAP), Frequency Stability

1. INTRODUCTION

Power supply to critical process loads is extremely important for an industrial cogeneration power

plant not only for continuous production but also important for overall plant safety during severedisturbances. A sudden interruption in production may result in significant economic loss and

raise safety concern. Most of the industrial customers with requirement of uninterrupted input of energy in the form of electric power and steam have installed cogeneration units. The

cogeneration systems are broadly defined as the coincident or simultaneous generation of the

combined heat and power (CHP) [1]. By installing cogeneration units industrial customer canachieve better efficiency of energy usage and enhance the reliability of electricity power supply

[2]. The cogeneration has to be tied together with the Public Power Company (PPC) to cover themismatch of load demand and power output by the cogeneration units in the plant and for the

consideration of power quality. The several techno-economic studies [3-5] are requiredperiodically throughout the operating life of the plant to ensure that a cogeneration plant will

operate safely, reliably, and economically.

To prevent total blackout and to stabilize the system under any abnormal condition, appropriateIslanding and Load Shedding (LS) strategies must be developed for industrial cogeneration

system. The load shedding technique primarily can be classified as conventional load sheddingtechnique and Adaptive or Intelligent load shedding technique. Conventional load shedding

schemes, breaker interlock load shedding [11], under-frequency relay (81) load Shedding [6, 7,

10, 11], programmable logic controller-based load shedding are most common and easy way toisolate the excess amount of load during generation deficit in the islanded power system. The




36

conventional schemes are designed to work on worst system operating conditions. These schemes

are not include, real-time system configuration, type and duration of the disturbances, as well asother important information and total loss of the system is an assumed possibility [14].

Conventional methods of system load shedding are too slow and do not effectively calculate the

correct amount of load to be shed.

Several schemes are reported in the literature [12-17] to overcome the shortcomings of theconventional load shedding schemes, by making it adaptive through complete understanding of

power system dynamics. In this paper ANN based load shedding method in comparison to under

frequency relay based load shedding is described. In order to illustrate the effectiveness of theproposed ANN based load shedding approach for the islanded ICP system, a large Oil Storage

Terminal and refinery distribution system is investigated as a case study. Section 2 introducessystem description and configuration of the ICP system. In Section 3, power studies are

conducted, the frequency response of the system is analyzed. Section 4 illustrates ANN basedload shedding, the 81-relay based and ANN based load shedding methods are compared in section

5 and concluded in section 6.

2. SYSTEM DESCRIPTION

In order to determine transient performance of a power system, the sub-transient model of thegenerators, IEEE standard model of exciter and governor control systems of the cogeneration unit

and static and dynamic model of loads have considered. The single line diagram (SLD) of ICPpower distribution system is shown in figure 1. The industrial cogeneration is simulated on ETAP

software.

Figure.1 Single line diagram for the OTS




37

To provide continuous power supply for critical loads and enhance overall efficiency of the plant,

ICP has installed two 10MW steam based cogeneration units STG-1 and STG-2. Both generatingunits in study are represented by detailed model with transient and subtransient circuits on both

the direct and quadrate axes. The power is generated at 11kV and step-down to 6.6kV by

generator transformers GT-301 and GT-302 and connected to Switchgear SG-301B. The block of the IEEE type 1 excitation model used for the both generators is as shown in figure 2. To achieve

the quick response of the cogeneration unit output to the external disturbance, the single reheatsteam governor-turbine system model as shown in figure 3 is used.

1

1 RsT + 1

A

A

K

sT +

1

E E K sT +

1

F

F

sK

sT +

( ) E fd S f E =

t V fd E +

+

ref V

maxVR

minVR

∑∑

Figure 2. Excitation system for SGT-1 and SGT-2

+

+

ref W

W 1 sr

K

sT +

P

1

1 chsT +

1

1 csT +

maxP

minP

mP1

1

hp

rh

F

sT

−

+

hpF

+

+

∑ ∑

∑

P Pe= I sochref P

Figure 3. Governor system for STG-1 AND STG-2

To improve system reliability and power quality, the ICP has Grid connectivity with public powercompany (PPC) at two points. PPC power is available at 66 kV voltage level through utility ties

UTG-1 and UTG-2. Two on load tap changer grid transformers TR-301 and TR-302 step downthe voltage from the 66 kV to the 6.6 kV level and connected to Switchgear SG-301A through

cable. The Switchgear SG-401A, SG-401B, Motor Control Center MCC-401, Power and MotorControl Center PMCC-402 are supplied through transformers TR-401, TR-402, TR-403 and TR-

404 of 6.6/0.44 kV respectively. The motors above 160kW rating are connected to switchgearSG-401A and SG-401B at 6.6kV voltage level. During islanded condition, due to a utility service

outage, in-plant generators will supply power to the plant load. The rated capacity of each

generator is 10 MW and the total load connected is approximate 28.5 MW.

3. SYSTEM FREQUENCY RESPONSE ANALYSIS

The dynamic performance of the system with respect to change in total generation and load canbe represented by swing equation [17]. The relationship that define variation of frequency withtotal generation and load mismatch can be obtain from swing equation,

ଶ

ଶ= (1)




38

Where,

G: nominal MVA of generatorH: inertia constant

δ: generator rotor angle

f 0: nominal frequencyPa : net accelerated or decelerated power (mismatch between generation and load)

Consider the generator speed variation due to a disturbance which is given by,

= + = 2

Where is the synchronous speed in rad/sec

Differentiating above equation with respect to time,

=ଶ

ଶ= 2 (2)

Substituting equation (2) in equation (1), we get

=

2(3)

Equation (3) defines the rate of change of frequency in Hz with, total power mismatch Pa, system

nominal frequency f 0, and inertia constant H. The equation (3) can be used for an individualgenerator as well as for an equivalent generation in the system. For equivalent case, the inertia

constant (H) can be derived from the following,

= ଵ ଵ+

ଶ ଶ+ ⋯ +

ଵ + ଶ + ⋯ +

Where n is the number of generators in a power system

The average rate of change of system frequency can be calculated by equation (4)

2 1

2

2

2

1

( )

1

f f df pL

dt H f

f

−=

−

(4)

Where

p = average power factor; H= inertia constant

L= total lost generation/ total available generationf 1 = nominal frequency; f 2 = minimum allowable frequency




39

To find the proper relay setting for tie line tripping during fault in utility system and load

shedding design to enhance system performance of an ICP power system during load generationmismatch, detail power system studies are required [20]. The computer simulations of load flow

analysis, short circuit analysis and stability analysis for all possible load-generation scenarios and

network configurations has been executed on ETAP software. The network configuration duringnormal operation is shown in figure 1. The load flow result for rated operating condition is shown

in Table 1.

Bus No. Bus

in kV

Voltage Magnitude

in %age

Voltage

Angle

Gene

(kW)

Gene

(kVAr)

Load

(kW)

Load

(kVAr)

SG-301A 6.6 98.53 -1.71 9868 3882 11066 4661

SG-301B 6.6 98.53 -1.71 18000 9432 10220 4241

SG-401A 0.415 101.1 -4.04 0 0 1243 732

SG-401B 0.415 101.1 -4.04 0 0 1290 892

MCC-401 0.415 98.4 -5.25 0 0 284 215

PMCC-

0.415 99.5 -5.32 0 0 3521 1778

Table 1. Load flow report

The system frequency response without load shedding, for different generation-load scenarios andcontingencies, as given in Table 2 is shown in figure 4. The contingencies considered for study

includes, three phase fault in utility system, loss of utility supply and loss of generators STG-1.

Bus frequency which starts falling after the tie CBs trip continuously along as the generator loaddeficit exists.

Scenarios Total Generation(MW)

Total Load(MW)

Mismatch(MW)

Average

Scenario-1 19 20.786 1.786 -0.39

Scenario-2 17 20.486 3.486 -0.76

Scenario-3 15 24.206 9.206 -2.01Scenario-4 12 24.626 12.626 -2.75

Table 2. Generation and load mismatch at the time of tie-line trip

Figure 4. System frequency response for different load generation scenarios

0 5 10 1550

60

70

80

90

100

110

Time(sec)

F r q u e n c y ( i n % a g e )

Scenario 1

Scenario 2Scenario 3

Scenario 4




40

4. ANN BASED ADAPTIVE LOAD SHEDDING

An artificial neural network (ANN) is a flexible mathematical model consists of aninterconnected group of artificial neurons which is used for modelling complex nonlinear

relationships between input and output data sets [19]. Commonly neural networks are trained, so

that a particular input leads to a specific target output. Once the ANN has been trained it can beused to classify unknown patterns.

Figure 5 depicts three layer feedforward neural each layer has a weight matrix W , a bias vector b,and an output vector a. Where the input signal p with R variables is expressed as [p1, p2, p3,

……pR]T

. Input-output data sets which are used to training, testing and validation of ANN are

{(p1, q1), (p2, q2), (p3, q3), ……(pR, qR)}. Where q is desired output with R variables is calculatedby system analysis.

1,11,1iw

1,1,S Riw

21b

22

S b

22b

33

S b

32b

31b

11n

2

2n

22

S n1

1

S n

1

2n

31n

21n

33

S n

32n

11a

3

3

S a

3

2a

22

S a

2

2a

31a

21a

11

S a

1

2a

2,11,1lw

2 1

2,1

,S S lw

3,21,1lw

3 2

3,2

,S S lw

( )11 1,1 1

IW p b f a = + ( )22 2,1 1 2 LW a b f a = + ( )33 3,2 2 3 LW a b f a = +

1 f

2 f

3 f

1

1

S b

12b

11b

1 f

1 f

2 f

2 f

3 f

3 f

1 p

1 R

p

3 p

2 p

( )( )( )3 3 3,2 2 2.1 1 1,1 1 2 3a f LW f LW f IW p b b b= + + +

Figure 5. Multi-Layer Feed Forward Neural Network

The Levenberg–Marquardt Back-Propagation (LMBP) algorithm is used for training of the ANN

model because of the low error and least epochs. To prepare the training data sets for ANN, thetransient stability analysis has been performed to solve the minimum load shedding for various

operating scenarios with the help of ETAP software.

The data is transmitted from the input layer, multiplied by their respective weights, to the hiddenlayers before reaching the final output layer. The error signals between the target and actual

output at the output layer neurons are then propagated back to the hidden and input layers. Thesum of square error is then minimized by adjusting the synaptic weights and bias in any layers

during the training process of ANN model. For a multi-layer network, the net input nk+1

(i) andoutput a

k+1(i) of neuron i in the k+1 layer can be expressed as:

1 1 1

1

( ) ( , ) ( ) ( )sk

k k k k

j

n i w i j y j b i+ + +

=

= +∑ (5)

1 1 1( ) ( ( ))k k k a i f n i+ + +

= (6)

By representing the sum of the output square error as the performance index for the ANN, theerror function is given by

1 1

1 1( ) ( ) ( )

2 2

R Rk k T T

r r r r r r r r

E e eq qa a= =

= − − =∑ ∑ (7)



Electrical and Electronics Engi

Wherek

r r r e q a= − is the outp

Marquardt algorithm is used to

The Simulation diagram of ANconstant block x1 which is the

process input block the removeThe remove constantrows procesConstant values do not providproblems for some algorithms.

Figure 6. Simu

Figure 7. Regressi

The mapminmax processes inp

range [-1 1]. The processed inpby the scalar weight w {1,1}, to

b {1} that is simply being added

The bias is much like a weight,TANSIG symmetric sigmoid t

layer's net input into its net outp

neering: An International Journal (ELELIJ) Vol 2, No 2,

t error andk

r a is the final output of the rth

input. Th

inimize the mean square error function in Eq. (7)

N in MATLAB is shown in figure 6. The inputprocessed and normalized by process input bloc

constantrows and mapminmax processes are beinses input and target data by removing rows with coe a network with any information and can cau

link block diagram of ANN load shedding scheme

on plot for 20 & 10 neurons in 1st & 2nd hidden layer

t and target data by mapping it from its original

t is transmitted through a connection that multipli

form the product wp, again a scalar. The neuron hato the product if input and weight (wp) at the sum

xcept that it has a constant input of 1. The sum is tansfer function. Transfer functions convert a ne

ut. The output of this first hidden layer is then treat

ay 2013

41

e Levenberg–

is fed to thek. Inside this

g performed.nstant values.se numerical

range to the

s its strength

a scalar biasing junction.

en fed to theural network

d as input to




42

the second hidden layer where the same repeats with the same TANSIG transfer function. The

output of the second hidden layer acts as the input to the output layer and is multiplied by weightw {3,2} added by bias b{3} and fed to purelin transfer function to get the output. This output is

then processed again to convert it back to the original form using mapminmax_reverse and

remove constantrows_reverse process. The output can be viewed in the scope y1.

PG (kW) PL (kW) PS (kW) df/dt PDLS (kW) PANN (kW) % Error

19000 27763 1000 -1.91 8097 8050 -0.16

18000 26822 2000 -2.14 6550 6588 0.13

18000 23378 2000 -1.3 2770 2862 0.32

18000 21016 2000 -0.73 710 722 0.04

17000 25455 3000 -1.84 6261 6228 -0.11

17000 23180 3000 -1.35 3532 3622 0.31

17000 19468 3000 -0.54 0 134 0.47

16000 26806 4000 -2.94 6660 6669 0.03

16000 22364 4000 -1.73 2320 2292 -0.09

16000 19096 4000 -0.84 0 8 0.0315000 25410 5000 -3.02 6620 6375 -0.86

15000 23322 5000 -2.42 4660 4620 -0.14

15000 19878 5000 -1.42 900 906 0.02

14000 21938 6000 -2.47 4150 4182 0.11

14000 20960 6000 -2.17 3170 3206 0.12

14000 17964 6000 -1.23 160 204 0.15

13000 21420 7000 -2.82 4620 4643 0.082

13000 18550 7000 -1.86 1600 1588 -0.04

13000 16584 7000 -1.2 0 25 0.08

12000 20012 8000 -2.91 4220 4212 -0.02

12000 19482 8000 -2.71 3600 3577 -0.08

12000 16926 8000 -1.79 960 865 -0.33

11000 18312 9000 -2.9 3510 3516 0.02

11000 16604 9000 -2.22 1640 1628 -0.04

11000 15772 9000 -1.89 770 803 0.11

10000 14936 10000 -2.15 960 925 -0.12

10000 14440 10000 -1.94 400 433 0.11

10000 13820 10000 -1.67 0 -120 -0.42

9000 14768 11000 -2.79 1915 1928 0.05

9000 14240 11000 -2.54 1290 1295 0.01

9000 12472 11000 -1.68 0 -20 -0.07

Table 3. ANN-based load shedding results

The number of neurons in input layer is equal to the number of inputs i.e. 4 while the output layer

has one neuron. The selection of number of neurons for the two hidden layer is made on hit andtrial method basis, comparing the regression plot of each and choosing the best among them. The




43

best performance is obtained with 20 neurons in 1st

hidden layer and 10 neurons in 2nd

hidden

layer. The regression plot is shown in figure 7.By selecting the total in-plant generation, total load, spinning reserve of generators and rate of

change of frequency as the input neurons of the ANN, the minimum amount of load shedding is

determined. The input signal p with four variables is expressed as [ , , ௦ , ⁄ ]் and one

output q as PDLS

.

The transient stability analysis for 121 load-generation scenarios have been carried out, with the

values of inputs PG, PS, PL, and df/dt varied between 9000–18000KW, 2000-10000KW, 19468-27868KW, and 0.54-3.02Hz/s respectively and ANN targets PDLS are determined. The 80% of the

total cases is selected for the ANN training, 10% for testing and 10% for validation. Thecorresponding load shedding amount as calculated by ANN with LMBP algorithm (PANN) for 31

load-generation scenarios and fault in utility system with tie-line trip is shown in Table 3.

4. COMPARISON OF LOAD SHEDDING METHODS

To demonstrate the effectiveness of the proposed methodology, system under study has beenmade to undergo a fault contingency and load shedding is performed by underfrequency relay and

ANN based adaptive load shedding method. The underfrequency relays settings for the first-stepload shedding will be activated simultaneously upon loss of the tie line. Underfrequency load

shedding design, number of steps, step frequency, and percentage load shedding amount for 81Lrelay is shown in Table 4.

Steps Threshold frequency %age LS Time Delay (sec)

Step-1 49.5 40 0.1

Step-2 48.5 30 0.1

Step-3 48 30 0.1

Table 4. Underfrequency relay setting for load shedding

Figure 8 depicts frequency response of islanded system with 15000KW generation and 25410KW

load, with underfrequency relay based load shedding and ANN based load shedding methods. Inthe case of underfrequency load shedding, at t=0.5 sec three phase fault is created in utility grid

which is cleared by opening tie-line Circuit Breaker (CB-1 and CB-2) at 0.6 sec. As the system

frequency reaches below 49.5 Hz at 1 sec, underfrequency relay is activated and first stage loadshedding is implemented at 1.1 sec.

Figure 8. System frequency with different load shedding methods

0 5 10 1 5 20 25 3097.5

98

98.5

99

99.5

100

100.5

101

101.5

Time(sec)

F r e q

u e n c y ( i n % a g e )

Relay Based UFLS

ANN Based UFLS




44

The system frequency still not recovered and it crosses 49 Hz (second step load shedding

threshold) and second step load shedding is implemented at 1.46 sec. After 2-step load sheddingsystem frequency is improved upto 49.5 Hz in 3.65 sec. It was found that the total amount of loadshed using underfrequency relay method is 11500 KW. For the same contingency in case of ANN

based adaptive load shedding, after the opening of tie-line Circuit Breaker (CB-1 and CB-2) at 0.6

sec the entire load shedding is performed at 0.74 sec with the calculation delay of 0.02 sec

included. The amount of load shedding is 6620 KW of load which is 4880 KW less than theconventional scheme. The system electrical power and mechanical power variations for both the

methods of load shedding is as shown in figure 9 and figure 10 respectively.

Figure 9. System electrical power with different load shedding methods

Figure 10. System mechanical power with different load shedding methods

5. CONCLUSION

In this paper an approach for improvement of frequency stability using ANN based adaptive

minimum load-shedding scheme is developed for industrial cogeneration system. By executing

the transient stability analysis for various operation scenarios of the ICP system, the training dataset of ANN model, which includes, total system power generation, spinning reserve, total load,

0 5 10 15 20 25 300

2

4

6

8

10

12

14

Time(sec)

E l e c t r i c a l P o w e r ( M W )

ANN Based UFLS

Relay Based UFLS

0 5 10 157

7.5

8

8.5

9

9.5

10

Time(sec)

M e c h a n i c a l P o w e r ( M W

)

ANN Based UFLS

Relay Based UFLS




45

and frequency decay rate as input, and the minimum amount of load shedding required as output,

has been prepared. To verify the effectiveness of the proposed ANN based load shedding ascompare to the present underfrequency relay based load-shedding, schemes are applied in the

simulation on ETAP software to investigate the dynamic response of system frequency. It is

concluded that the proposed ANN based methodology with two hidden layers and LMBPalgorithm can achieve more effective load shedding to maintain system stability as compare to

underfrequency based relay.

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Authors

Mukesh Kumar Kirar was born in Narsinghpur, India, in 06 Feb 1983. He received the

B.E. (Electrical) degree from Government Engg. College, Ujjain, India in 2006 and

M.Tech. (Power System) in 2008 and pursuing Ph.D from MANIT Bhopal, India. He is

currently working as an assistant professor in the Department of Electrical Engineering,

MANIT, Bhopal, India. His field of interests are power system stability and control,

transformers and machines.

Renuka Kamdar was born in Bhopal, India in 1987. She has received BE degree (2009)

in Electrical and Electronics Engineering from Oriental Institute of Science and

Technology Bhopal and pursuing her M. Tech degree in Power System from MANIT

Bhopal.

Ganga Agnihotri was born in Sagar, India, in 27 May 1949. She received the B.E.

(Electrical) degree from MACT, Bhopal, India. She received the M.E. (Advance

Electrical Machine) and PhD (Power System Planning Operation and Control) from

University Of Roorkee, Roorkee in 1974 and 1989 respectively. She is currently

working as a professor in the Department of Electrical Engineering, MANIT, Bhopal,

India. She has 12 research papers in International journals, 20 research papers inNational journals, 22 research papers in International Conferences and 70 research

papers in National Conferences. Her fields of interest are Power System Planning,

Power Transmission Pricing, Power System Analysis and Deregulation. Dr. Agnihotri has a membership of

Fellow IE(I) and LISTE.

load shedding design for an industrial cogeneration system

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