Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013
LOAD SHEDDING DESIGN FOR AN INDUSTRIAL
COGENERATION SYSTEM
Mukesh Kumar Kirar1, Renuka Kamdar2, Manoj Kumar3, Ganga Agihotri4
1
Department of Electrical Engineering, MANIT, Bhopal, India
mukeshkirar@rediffmail.com, renukakamdar_123@yahoo.co.in,
manoj11manit@gmail.com, ganga1949@gmail.com
ABSTRACT
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.
KEYWORDS
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 severe
disturbances. 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 can
achieve 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 the
mismatch 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 required
periodically 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, appropriate
Islanding and Load Shedding (LS) strategies must be developed for industrial cogeneration
system. The load shedding technique primarily can be classified as conventional load shedding
technique 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 to
isolate the excess amount of load during generation deficit in the islanded power system. The
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Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013
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 as
other 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 the
conventional 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 the
proposed 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 introduces
system 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 based
load 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 the
generators, 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 ICP
power distribution system is shown in figure 1. The industrial cogeneration is simulated on ETAP
software.
Figure.1 Single line diagram for the OTS
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Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013
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 generating
units 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 reheat
steam governor-turbine system model as shown in figure 3 is used.
Vref
1
1 + sTR
Vt
−
+
∑
−
SE = f (Efd)
VRmax
KA
1 + sTA
∑
−
1
K E + sTE
+
E fd
VRmin
sK F
1 + sTF
Figure 2. Excitation system for SGT-1 and SGT-2
Wref
P
P
W
+
∑
−
max
K
1 + sTsr
+
∑
1
1 + sTc
P = Pe
Pref
Pmin
I soch
Fhp
1
1 + sTch
+
1 − Fhp
1 + sTrh
∑
+
Pm
Figure 3. Governor system for STG-1 AND STG-2
To improve system reliability and power quality, the ICP has Grid connectivity with public power
company (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 down
the 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 Motor
Control Center PMCC-402 are supplied through transformers TR-401, TR-402, TR-403 and TR404 of 6.6/0.44 kV respectively. The motors above 160kW rating are connected to switchgear
SG-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 can
be represented by swing equation [17]. The relationship that define variation of frequency with
total generation and load mismatch can be obtain from swing equation,
= (1)
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Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013
Where,
G: nominal MVA of generator
H: inertia constant
δ: generator rotor angle
f0: nominal frequency
Pa : 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,
= = 2 (2)
Substituting equation (2) in equation (1), we get
=
(3)
2
Equation (3) defines the rate of change of frequency in Hz with, total power mismatch Pa, system
nominal frequency f0, and inertia constant H. The equation (3) can be used for an individual
generator as well as for an equivalent generation in the system. For equivalent case, the inertia
constant (H) can be derived from the following,
=
+ + ⋯ + + + ⋯ + 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)
df pL ( f 2 − f1 )
=
dt H  f 22 
1 − 2 
 f1 
(4)
Where
p = average power factor; H= inertia constant
L= total lost generation/ total available generation
f1 = nominal frequency; f2 = minimum allowable frequency
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Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013
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 generation
mismatch, 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 during
normal operation is shown in figure 1. The load flow result for rated operating condition is shown
in Table 1.
Bus No.
Bus
in kV
6.6
6.6
0.415
0.415
0.415
0.415
SG-301A
SG-301B
SG-401A
SG-401B
MCC-401
PMCC401
Voltage Magnitude Voltage
in %age
Angle
98.53
-1.71
98.53
-1.71
101.1
-4.04
101.1
-4.04
98.4
-5.25
99.5
-5.32
Gene
(kW)
9868
18000
0
0
0
0
Gene
(kVAr)
3882
9432
0
0
0
0
Load
(kW)
11066
10220
1243
1290
284
3521
Load
(kVAr)
4661
4241
732
892
215
1778
Table 1. Load flow report
The system frequency response without load shedding, for different generation-load scenarios and
contingencies, 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 load
deficit exists.
Scenarios
Scenario-1
Scenario-2
Scenario-3
Scenario-4
Total
(MW)
19
17
15
12
Generation Total
(MW)
20.786
20.486
24.206
24.626
Load Mismatch
(MW)
1.786
3.486
9.206
12.626
Average -0.39
-0.76
-2.01
-2.75
Table 2. Generation and load mismatch at the time of tie-line trip
110
Frquency(in %age)
100
90
80
70
Scenario 1
Scenario 2
Scenario 3
Scenario 4
60
50
0
5
10
15
Time(sec)
Figure 4. System frequency response for different load generation scenarios
39
Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013
4. ANN BASED ADAPTIVE LOAD SHEDDING
An artificial neural network (ANN) is a flexible mathematical model consists of an
interconnected 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 be
used 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 calculated
by system analysis.
2,1
1
n1
1,1
iw1,1
p1
1
2
a
1
f
1
2
n
1,1
S,R
iw
f
1
a
1
1
S1
n
lw
b S1
1
( IW 1,1 p + b1 )
3
a = f
3
( LW
2,1
S 2 ,S1
a
3,2
f
a
2
f
f
2
a
=
2
( LW
2,1 1
2
( LW
2.1 1
( IW
a +b
1,1
2
2
S2
f
3
a2
f
3
a S3
3
)
3,2
S 3 ,S 2
a
1
)
p+b +b
3
n S3
lw
2
b S2
f
a1
3
n2
2
2
b2
2
S2
2
f
3
3
b2
1
S1
3
f
3
2
2
2
1
f
3
n1
b1
n
b2
=
2
a1 lw1,1
2
b1
n
1
f
2
1
a
3,2
2
n1
b1
p2
p3
p R1
1
a1 lw1,1
1
f
2
3
=
3
3
bS3
f
3
( LW 3,2a 2 + b3 )
)+b )
3
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, the
transient 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 hidden
layers 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. The
sum 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) and
output ak+1(i) of neuron i in the k+1 layer can be expressed as:
sk
n k +1 (i ) = ∑ wk +1 (i, j ) y k ( j ) + b k +1 (i )
(5)
j =1
a k +1 (i) = f k +1 (nk +1 (i))
(6)
By representing the sum of the output square error as the performance index for the ANN, the
error function is given by
E=
1 R
1 R
∑ (q r − a kr )T (q r − a kr ) = ∑ (er )T er
2 r =1
2 r =1
(7)
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Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013
Where er = qr − a rk is the output error and a rk is the final output of the rth input. The Levenberg
Levenberg–
Marquardt algorithm is used to minimize the mean square error function in Eq. (7)
The Simulation diagram of ANN in MATLAB is shown in figure
fi
6.. The input is fed to the
constant block x1 which is then processed and normalized by process input block. Inside this
process input block the remove constantrows and mapminmax processes are being performed.
The remove constantrows processes input and target data by removing rows with constant values.
Constant values do not provide a network with any information and can cause numerical
problems for some algorithms.
Figure 6.. Simulink block diagram
diag
of ANN load shedding scheme
Figure 7.. Regression plot for 20 & 10 neurons in 1st & 2nd hidden layer
The mapminmax processes input and target data by mapping it from its original range to the
range [-11 1]. The processed input is transmitted through a connection that multiplies its strength
by the scalar weight w {1,1}, to form the product wp, again a scalar. The neuron
neuron has a scalar bias
b {1} that is simply being added to the product if input and weight (wp) at the summing junction.
The bias is much like a weight, except that it has a constant input of 1. The sum is then fed to the
TANSIG symmetric sigmoid transfer function. Transfer functions convert a neural network
layer's net input into its net output. The output of this first hidden layer is then
then treated as input to
41
Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013
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 weight
w {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)
19000
18000
18000
18000
17000
17000
17000
16000
16000
16000
15000
15000
15000
14000
14000
14000
13000
13000
13000
12000
12000
12000
11000
11000
11000
10000
10000
10000
9000
9000
9000
PL (kW)
27763
26822
23378
21016
25455
23180
19468
26806
22364
19096
25410
23322
19878
21938
20960
17964
21420
18550
16584
20012
19482
16926
18312
16604
15772
14936
14440
13820
14768
14240
12472
PS (kW)
1000
2000
2000
2000
3000
3000
3000
4000
4000
4000
5000
5000
5000
6000
6000
6000
7000
7000
7000
8000
8000
8000
9000
9000
9000
10000
10000
10000
11000
11000
11000
df/dt
-1.91
-2.14
-1.3
-0.73
-1.84
-1.35
-0.54
-2.94
-1.73
-0.84
-3.02
-2.42
-1.42
-2.47
-2.17
-1.23
-2.82
-1.86
-1.2
-2.91
-2.71
-1.79
-2.9
-2.22
-1.89
-2.15
-1.94
-1.67
-2.79
-2.54
-1.68
PDLS (kW)
8097
6550
2770
710
6261
3532
0
6660
2320
0
6620
4660
900
4150
3170
160
4620
1600
0
4220
3600
960
3510
1640
770
960
400
0
1915
1290
0
PANN (kW)
8050
6588
2862
722
6228
3622
134
6669
2292
8
6375
4620
906
4182
3206
204
4643
1588
25
4212
3577
865
3516
1628
803
925
433
-120
1928
1295
-20
% Error
-0.16
0.13
0.32
0.04
-0.11
0.31
0.47
0.03
-0.09
0.03
-0.86
-0.14
0.02
0.11
0.12
0.15
0.082
-0.04
0.08
-0.02
-0.08
-0.33
0.02
-0.04
0.11
-0.12
0.11
-0.42
0.05
0.01
-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 and
trial method basis, comparing the regression plot of each and choosing the best among them. The
42
Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013
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 [ , " , # , ⁄
]& 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, 1946827868KW, 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. The
corresponding 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 been
made 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-step
load 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 81L
relay is shown in Table 4.
Steps
Step-1
Step-2
Step-3
Threshold frequency
49.5
48.5
48
%age LS
40
30
30
Time Delay (sec)
0.1
0.1
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. In
the 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 load
shedding is implemented at 1.1 sec.
101.5
101
Frequency(in %age)
100.5
100
99.5
99
Relay Based UFLS
ANN Based UFLS
98.5
98
97.5
0
5
10
15
20
25
30
Time(sec)
Figure 8. System frequency with different load shedding methods
43
Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013
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 shedding
system frequency is improved upto 49.5 Hz in 3.65 sec. It was found that the total amount of load
shed 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 the
conventional 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.
14
Electrical Power(MW)
12
10
8
6
4
ANN Based UFLS
Relay Based UFLS
2
0
0
5
10
15
20
25
30
Time(sec)
Figure 9. System electrical power with different load shedding methods
10
Mechanical Power (MW)
9.5
9
8.5
8
7.5
ANN Based UFLS
Relay Based UFLS
7
0
5
10
15
Time(sec)
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 data
set of ANN model, which includes, total system power generation, spinning reserve, total load,
44
Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 2, May 2013
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 as
compare 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 LMBP
algorithm 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 in
National 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.
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