Transient stability enhancement by ann based

INTERNATIONALIssue 3, October – December (2012), © IAEME 0976 – 6545(Print), ISSN
International Journal of Electrical Engineering and Technology (IJEET), ISSN
0976 – 6553(Online) Volume 3,
JOURNAL OF ELECTRICAL ENGINEERING
& TECHNOLOGY (IJEET)
ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 3, Issue 3, October - December (2012), pp. 200-210
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TRANSIENT STABILITY ENHANCEMENT BY ANN BASED
ADAPTIVE LOAD SHEDDING

Mukesh kumar Kirar1, Ganga Agnihotri2
1
(Electrical Engineering Department, MANIT, Bhopal, India, mukeshkirar@rediffmail.com)
2
(Electrical Engineering Department, MANIT, Bhopal, India, ganga1949@gmail.com)

ABSTRACT

This paper presents frequency stability enhancement using Artificial Neural Network
based adaptive load shedding during the various fault contingencies. At any given moment,
the electrical power produced by the generators must be equal to the electrical power
consumed by the network to continue stable operation. Every change in this balance that
disturbs the steady-state operation of the power system is referred to as a power imbalance.
This power imbalance in the network leads frequency instability. To ensure system stability
and availability during disturbances, generation trip, transmission and distribution equipment
failure, generally utilize some type of load shedding scheme. Conventional non-adaptive
load-shedding algorithm is not the most efficient scheme for all power system disturbances.
Artificial Neural Network based load shedding method is employed to calculate the minimum
amount of load to be shed due to the effect of the contingencies, to improve the transient
stability of the system. The IEEE-9 bus test system is simulated on ETAP software and
Transient stability is analyzed by considering various contingencies on test system such as
generator outage, faults on transmission lines & buses, transformers etc. Artificial Neural
Network (ANN) has been implemented on MATLAB.

Keywords: Transient stability, Artificial Neural Network, ETAP, MATLAB, load shedding.

1. INTRODUCTION

To prevent the complete blackout of the power system during power imbalance
condition, it is necessary to curtail partial loads in the plant to maintain the balance between
available generation and load as well as to restore the system frequency [1-4]. The load
shedding technique primarily can be classified as convention load shedding technique and
Adaptive or Intelligent load shedding technique. Conventional load shedding schemes,
Breaker Interlock Load Shedding [4], Under-Frequency Relay (81) Load Shedding [5-8],
Programmable Logic Controller-Based LS are most common and easiest way to isolate the
excess amount of load during generation deficit in the power system. These methods of load
shedding are totally independent of the system dynamics, Pre-disturbance operating
200

International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN
0976 – 6553(Online) Volume 3, Issue 3, October – December (2012), © IAEME

conditions, Post-disturbance operating conditions, Nature and duration of the disturbance,
System transient response to a disturbance, and total loss of the system is an assumed
possibility.
Conventional methods of system load shedding are too slow and do not effectively
calculate the correct amount of load to be shed because the amount of load shedding is
calculated for the worst-case scenario. [11]. Several schemes are reported in the literature [9-
18] to overcome the shortcomings of the conventional LS scheme, by making it adaptive
through complete understanding of power system dynamics and process constraints,
combined with knowledge of system disturbances[12].
The best load shedding scheme in power systems is one that is able to separate the
least possible loads of the network in the shortest time by considering power system
constraints. Artificial neural Network (ANN) have attracted a great deal of attention in the
past two decades in the area such as power system stabilizer [13-16], power system security
[17-21], harmonic detection [22-24], power system protection [25-29] and load Forecasting
[30-33]. In this paper adaptive load shedding strategy by executing the Artificial Neural
Networks is used to improve transient stability analysis for IEEE-9 bus system. The fast
calculation time is an important advantage of artificial neural-network based adaptive load
shedding technique compared to other methods. The inputs to Adaptive load shedding
methods is provided through Conventional Supervisory Control Data Acquisition/ Energy
Management System, which runs at periodicity of few minutes and data scan rate of 2-10 sec.
2. SYSTEM SIMULATION AND STUDIES
IEEE 9 bus system is used as the test system in this paper. The test system is
simulated on ETAP 7.5.1. The single line diagram (SLD) of the simulated test system on
ETAP is shown in Fig 1. For this test system generator and load parameters are given in
appendix. The total generation is 352.5MW and total load is 336.6MW. The test system
contains 6 lines connecting the bus bars in the system. The generator is connected to network
through step-up transformer at 230kV transmission voltage.
Power-system studies principally incorporate the techniques used to predict or
improve the performance of an existing or proposed power system under specified conditions
[34-36]. Power system studies include load flow, short circuit, protective device coordination,
transient stability, motor starting, grounding, transient overvoltage, power-factor
improvement. The results of the load flow and short circuit studies are inputs to the
equipment sizing and selection, reactive power compensation, stability analysis, protection
coordination etc. The results of load flow analysis and short circuit (SC) analysis when all
generators and loads are operating at rated power is given in Table.1 and Table.2
"
respectively. Table.2 represents short circuit analysis results, ‫ܫ‬௄ (subtransient symmetrical SC
currents), IP (peak SC currents) for 3-phase faults on buses.
The dynamic performance of the system with respect to change in total generation and
load can be represented by swing equation [38]. The relationship that define variation of
frequency with total generation and load mismatch can be obtain from swing equation,
ீு ௗ మ ఋ೘
గ௙బ ௗ௧ మ
ൌ ܲ௔ (1)
Where,
G: nominal MVA of generator
H: inertia constant
δ: generator rotor angle
f0: nominal frequency
Pa: net accelerated or decelerated power (mismatches between generation and load)

201


Fig. 1 Single Line Diagram of IEEE-9 Bus Test System

TABLE.1 LOAD FLOW REPORT
Bus No. Bus KV Voltage Voltage Gen. Gen. Load Load
Mag. (%) Angle (MW) (Mvar) (MW) (Mvar)
Bus 1 16.5 100.0 1.0 65.67 44.46 0 0
Bus 2 18.0 100.0 4.3 163.00 36.81 0 0
Bus 3 13.8 100.0 1.2 85.00 51.68 0 0
Bus 4 230 98.20 -0.5 0 0 0 0
Bus 5 230 98.10 -0.4 0 0 124.04 49.62
Bus 6 230 98.22 -0.5 0 0 89.32 29.77
Bus 7 230 98.30 -0.4 0 0 0 0
Bus 8 230 98.10 -0.4 0 0 99.25 34.74
Bus 9 230 98.30 -0.5 0 0 0 0

TABLE.2 SHORT CIRCUIT CURRENTS FOR 3-PHASE FAULT
"
Bus No. KV I K (KA) IP (KA)
Bus 1 16.5 78.37 205.73
Bus 2 18 104.66 280.53
Bus 3 13.8 110.29 288.72
Bus 4 230 10.37 25.25
Bus 5 230 10.44 25.69
Bus 6 230 10.36 25.19
Bus 7 230 10.47 25.99
Bus 8 230 10.42 25.47
Bus 9 230 10.38 25.37
202


Consider the generator speed variation due to a disturbance which is given by,
݀ߜ
߱ ൌ ߱଴ ൅ ൌ 2ߨ݂
݀‫ݐ‬
Where ߱଴ is the synchronous speed in rad/sec
Differentiating above equation with respect to time,
ௗఠ ௗమ ௗ௙
ௗ௧
ൌ ௗ௧ మ ൌ 2ߨ ௗ௧ (2)
Substituting equation (2) in equation (1), we get
ೌ బ ௗ௙ ௉ ௙
ൌ ଶீு ௗ௧
(3)
Equation (3) defines the rate of change of frequency in Hz with, total power mismatch Pa,
system nominal frequency f0, and inertia constant H. This equation can be used for an individual
generator or for an equivalent which represents the total generation in a system. For equivalent case,
the inertia constant (H) can be derived from the following,
‫ܪ‬ଵ ‫ܣܸܯ‬ଵ ൅ ‫ܪ‬ଶ ‫ܣܸܯ‬ଶ ൅ ‫ ڮ‬൅ ‫ܪ‬௡ ‫ܣܸܯ‬௡
‫ܪ‬ൌ
‫ܣܸܯ‬ଵ ൅ ‫ܣܸܯ‬ଶ ൅ ‫ ڮ‬൅ ‫ܣܸܯ‬௡
Where n is the number of generators in a power system.
System frequency response for different generation-load scenarios and contingencies as given
in table 3 is shown in fig. 2. The contingencies considered for study includes loss of generators G1, G2
and G3 for different load and generation scenarios. Bus frequency which starts falling after the
contingency continuously along as the generator load deficit exists. As the difference between available
generation and load increases decay rate increases.
TABLE.3 GENERATION AND LOADS SCENARIOS
Scenarios Total Generation Total Load Mismatch
Scenario-1 248.9 293.9 45.0

Scenario-2 238.9 288.9 50.0
Scenario-3 248 313.7 65.7
Scenario-4 228.7 313.7 85.0
Scenario-5 130 288.9 158.9
Scenario-6 150.7 313.7 163.0

100

90
Frequency (in %age)

80

70
scenario1
scenario2
60 scenario3
scenario4
scenario5
50 scenario6

40
0 2 4 6 8 10
Time (sec)

Fig. 2 System Frequency Response for Different Load Generation Scenario.
Underfrequency or the rate of frequency decline is used to determine overload or generation
and load mismatch in system. In a large system, there can be an almost infinite number of possibilities
that can result in load–generator imbalance. Load-Generation imbalance problem can be solved by
either generation control or load control. Load control or Load shedding (LS) is the last option to
maintain balance between load and generation.

203


3. ANN BASED ADAPTIVE LOAD SHEDDING
An artificial neural network (ANN) is a flexible mathematical structure which is capable of
identifying complex nonlinear relationships between input and output data sets. A neural network is a
parallel-distributed processor made up of simple processing units, is known as neurons, which has a
tendency for storing, and making easily available, experimental information.
A multi-layers network can have several layers, each layer has a weight matrix W, a bias
vector b, and an output vector a. The three-layer network is shown below in figure and in the
equations at the bottom of the figure.

1 1 2,1 2 2 3,2 3 3
1,1 n1 1 a1 lw1,1 n1 2 a1 lw1,1 n1 f
3 a1
iw1,1 f f
p1 b1
1 2
b1 3
b1 3
p2 n
1
2 1 a
1
2 n
2
2 2 a
2
2
n2 3
3
a2
f f f
p3 1 2 3
b2 b2 b2
p R1 n S1
1 1
nS2
2 2 3
n S3 3
a S3
1 a S1 2 a 3
1,1
f f S2 f
iw S , R 2,1
lw S 2 ,S1 3,2
1 2
lw S 3 ,S 2 3
b S1 b S2 bS3
3
a = f ( LW a + b )
3 3,2 2 3 2
a
1
= f ( LW 2,1a1 + b2 )
1
( IW 1,1 p + b1 ) a
2
= f
a = f ( LW f ( LW f ( IW p + b ) + b ) + b )
3
1,1 3 1 2 3,2
3 2 2.1 1

Fig.3 Multi-Layer Feed Forward Neural Network

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.
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 propagated from the input layer, multiplied by their respective weight, to the
hidden layers before reaching the final output layer. The error signals between the desire output and
actual output at the output layer 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 ) + bk +1 (i ) (4)
j =1

a k +1 (i ) = f k +1 (nk +1 (i )) (5)
By representing the sum of the output square error as the performance index for the ANN,
the error function is given by
1 R k k 1 R
E= ∑ (q r − a r )T (q r − a r ) = 2 ∑ (er )T er
2 r =1 r =1 (6)
Where er = qr − ark is the output error and ark is the final output of the rth input. The
Levenberg–Marquardt algorithm is used to minimize the mean square error function in Eq. (6)
The input-output data sets are generated by performing transient stability analysis for
different contingencies. In this paper the input variables of the ANN model are the total power
generation of the system (Pg), total load demand (PL) and the frequency decay rate (df/dt) and the
output is the minimum amount of load shedding (PLS).

204


4. IMPROVEMENT OF TRANSIENT STABILITY USING LOAD SHEDDING

The objective of the ANN based adaptive load shedding is to trip minimum required
load during generation deficit. An ANN-based system should be implemented to execute load
shedding in an automatic manner. When a fault occurs and results in generation shortage of
the system, the values of input neurons for ANN controller can be captured by the SCADA
system in real-time basis.
The results of the ANN load shedding analyses with 21 different scenarios of
generation and load conditions are shown in Table.4. For these study cases, the values of PG,
PL and df/dt are varied between 150–270 MW, 270-315 MW and 0.24-4.81Hz/s respectively.

TABLE.4 ANN-BASED LOAD SHEDDING RESULTS
Pg PL df/dt PLS PANN ERROR
(MW) (MW) (Hz/s) (MW) (MW)

270 315 -0.731 39.07 40.5 1.42
270 300 -0.486 27.40 27 -0.40
270 285 -0.240 13.17 13.5 -0.32
248 315 -0.930 60.58 60 -0.58
240 315 -1.366 66.76 67.5 0.73
240 300 -1.090 54.21 54 -0.21
240 285 -0.817 35.07 35 -0.07
240 270 -0.540 21.30 22 0.69
228 315 -1.664 87 87.25 0.25
210 315 -2.190 96.33 94.5 -1.83
210 300 -1.879 82.66 81 -1.66
210 285 -1.560 61.48 60.5 -0.98
210 270 -1.248 64.50 63 -1.51
180 315 -3.200 119.69 121.5 -1.00
180 300 -2.910 108.61 108 -0.61
180 285 -2.540 89.88 90 0.11
180 270 -2.190 81.11 81 -0.11
150 315 -4.810 180.21 180 -0.21
150 300 -4.380 149.63 148.5 -1.13
150 285 -3.942 115.64 115 -0.63
150 270 -3.500 107.32 108 -0.67

For a case, 228MW total generation, 36MW spinning reserve and 315MW total load
demand, conventional and ANN based Adaptive load shedding results are shown in fig. 4-5.
The conventional load shedding is performed by underfrequency relay.
205


Fig.4 Frequency plot for conventional load shedding
The underfrequency relay is set to operate at 58.5Hz and 58Hz for first and second
step load shedding respectively. For this condition total load shedding required by the
conventional load shedding and ANN method is 95 MW and 80 MW respectively.

Fig.5 Frequency plot for adaptive load shedding
5. CONCLUSION
In this paper an approach for improvement of frequency stability using adaptive
minimum load-shedding scheme by the ANN model is developed for IEEE 9 bus system. By
executing the transient stability analysis for various operation scenarios of the test system, the
training data set of ANN model, which includes of total system power generation, total
demand, frequency decay rate and the minimum amount of load shedding required has been
prepared. With the smallest training time and the least epochs, the LMBP algorithm is used to
derive the ANN model of minimum load shedding for the IEEE-9 bus test system. To verify
the effectiveness of the proposed ANN model for load shedding with system fault
contingency, traditional and adaptive load-shedding methods are applied in the simulation to
investigate the dynamic response of system frequency. It is found that the ANN based load
shedding method requires less load shedding amount as compare to conventional method for
the same fault contingency.

206


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APPENDIX

Generator Data
Parameter G1 G2 G3

Operation mode Voltage control Swing Voltage control
Rated MVA 80 220 110
KV 16.5 18 13.8
Power factor 0.90 0.85 0.85
Type Hydro Thermal Thermal
Speed 1500 1500 1500
'
Tdo 5.6 5.6 5.6
'
Tqo 3.7 3.7 3.7

Load Data
Load Rating (MVA) Rated KV
Lump1 71.589 230
Lump2 53.852 230
Lump3 55.902 230
Lump4 31.623 230
Lump5 20.616 230
Lump6 25.495 230
Lump7 31.623 230
Lump8 20.616 230
Lump9 25.495 230

210

Transient stability enhancement by ann based

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