Available transfer capability computations in the indian southern e.h.v power system using intelligent techniques

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 11 | Nov-2014, Available @ http://www.ijret.org 90
AVAILABLE TRANSFER CAPABILITY COMPUTATIONS IN THE
INDIAN SOUTHERN E.H.V POWER SYSTEM USING INTELLIGENT
TECHNIQUES
GopiKrishna P1
, Gowri Manohar T2
1
Research Scholar, Department of EEE, S.V. University, Tirupati, AP, India
2
Associate Professor, Department of EEE, S.V. University, Tirupati, AP, India
Abstract
This paper presents three methods for computing the available transfer capability (ATC). One method is the conventional method
known as continuation repeated power flow (CRPF) and other two are the intelligent techniques known as radial basis function
neural network (RBFNN), basic adaptive neuro fuzzy inference system (ANFIS). In these two intelligent techniques, the basic
ANFIS works with a multiple input single output (MISO) and it is modified as multiple input multiple output (MIMO) ANFIS using
the proposed MIMO ANFIS. The main intension of this proposed method is to utilise the significant features of ANFIS with
respect to the multiple outputs, as the basic ANFIS has been proved as the best intelligent techniques in the modelling of any
application, but it has a disadvantage of single output and this drawback will be overcome using the proposed MIMO ANFIS. In
this paper, the latest Indian southern region extra high voltage (SREHV) 72-bus system considered as test system to obtain the
ATC computations using three methods. The ATC computations at desired buses are obtained with and without contingencies and
compared the conventional ATC computations with the intelligent techniques. The obtained results are scrupulously verified with
different test patterns and observed that the accuracy of proposed method is proved as the best as compared to the other methods
for computing ATC. In this way, this paper shows a better way to compute ATC for the different open power market system
Keywords— Available Transfer Capability, Total Transfer Capability, Open Power Markets, Repeated Power Flow,
Radial Basis Function Neural Networks, Adaptive Neuro Fuzzy Inference System etc.
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1. INTRODUCTION
The time to time computations of available transfer capability
(ATC) becomes a significant and challenging issue in the
development of open power markets across the world [1]. To
solve this issue using the existing conventional methods such
as Repeated Power Flow (RPF) [2], Continuation Power
Flow (CPF), Transfer based Security Constrained Optimal
Power Flow (TSCOPF) [3], Optimal Power Flow
(OPF) ,Network Response method [4], Rated System Path
method [5], Linear ATC (LATC) method [6], ATC by
Comprehensive method [7] and DC Power Flow Method[8],
cannot be trusted in smooth operation of the power system
because these are depend on mathematical equations [9 ].
Even though these methods will provide the base information
related to the steady state operation of power system, they
fail to afford the on-line information due to the involvement
of iteration steps towards the convergent point. This
drawback would be overcome by intelligent techniques,
which are the most useful techniques to obtain precise and
necessary information when they are trained properly related
to the computations of ATC [10].
The ATC calculations play a vital role in the open power
market transactions and during blackouts the ATC between
two areas provides an indication of the amount of additional
electric power that can be transferred from one area to
another for a specific time frame, for a specific set of
conditions. ATC can be a very dynamic quantity because it is
a function of variable and interdependent parameters [11].
These parameters are highly dependent upon the conditions
of the network. Consequently, ATC calculations may need to
be periodically updated; it is possible only by using
intelligent techniques because of their proved performance in
the on-line conditions.
Artificial Neural Networks (ANN) is an emerging Artificial
Intelligence technique. An ANN is a massively parallel
information-processing system and it can perform nonlinear
computations in a short duration after it has trained with the
sufficient data. ANN has found many applications in Power
Systems [12]. With respect to all available intelligent
techniques, each individual technique will be strong enough
in its specific property and its implementation related to a
specific application. In this regard, the neural networks are
the best suitable in non-linear function related applications,
that is, they are the best for modelling of any application such
as voltage stability analysis, computations of available
transfer capability, load forecasting, power quality analysis,
power system fault diagnosis and so on. In the ANN, the
Radial Basis Function Neural Networks (RBFNN), Back
Propagation Neural Networks (BPNN) and Adaptive Neuro
Fuzzy Inference Systems (ANFIS) are the popular to model
the behaviour of any system. In this paper, the RBFNN and
ANFIS are used to compute the ATC.

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The remaining sections of this paper are organized as follows:
Section II briefly discusses the ATC. The section III
describes the RBFNN and ANFIS. The section IV deliberates
the methods used to compute ATC along with a proposed
MIMO ANFIS. The section V presents SREHV 72-bus test
system and different case scenarios to obtain the results from
three methods. Finally, section VI presents useful
conclusions.
2. AVAILABLE TRANSFER CAPABILITY
The Available Transfer Capability (ATC) is a measure of the
transfer capability remaining in the physical transmission
network for further commercial activity over and above
previously committed uses [13]. Mathematically, ATC is
defined as the Total Transfer Capability (TTC) less the
Transmission Reliability Margin (TRM) and the Capacity
Benefit Margin (CBM).
ATC = TTC - TRM – CBM (1)
Total Transfer Capability (TTC) is defined as the amount of
electric power that can be transferred over the interconnected
transmission network in a reliable manner while meeting all
of a specific set of defined pre and post-contingency system
conditions.
Transmission Reliability Margin (TRM) is defined as that
amount of transmission transfer capability necessary to
ensure that the interconnected transmission network is secure
under a reasonable range of uncertainties in system
conditions.
Capacity Benefit Margin (CBM) is defined as that amount of
transmission transfer capability reserved by load serving
entities to ensure access to generation from interconnected
systems to meet generation reliability requirements.
3. INTELLIGENT TECHNIQUES
In this paper, the following two intelligent techniques used to
compute available transfer capability.
3.1 Radial Basis Function Neural Networks (RBFNN)
The architecture of Radial Basis Function Neural Network
(RBFNN) consists of input layer, hidden layer and output
layer [14]. The RBFNN has become increasingly popular
because it is the best function approximation technique and is
used for modelling of any type of applications, which are
related to any field [15]. The RBFNN mainly uses the
activation function represented with equation (2) to its hidden
neurons.
)
2
)(
exp( 2
1
2





n
i
iji
i
wx
(2)
3.2 Training Algorithm of RBFNN
Step 1: Start training RBFNN.
Step 2: Declare and initialise all variables such as input
vector (xi), output of input layer Oil, output of hidden layer
Ohl, output of output layer Ool, targeted output vector (Oi),
initial assumed weights (Wij), change of weight (ΔWij),
hidden neuron’s activation function i , spread of RBFNN
( ), learning rate factor (η), error term (e), iteration number
(i) and number of epochs (n).
Step 3: Process the input vector xi to the input layer of
RBFNN and calculate the output of input layer using Oil =
f(xi )= m xi.
Step 4: Process the output of input layer Oil to the hidden
layer of RBFNN and calculate the output of the hidden layer
Ohl using equation 2.
Step 5: Process the output of hidden layer Ohl to the output
layer of RBFNN and calculate the output of output layer Ool
using equation 2.
Step 6: Calculate the error (e) and change in weight ΔWij(k)
using e = Oi – Ool and ΔWij(k) = η e xi
Step 7: Update initial weight matrix Wij using Wij (K+1) =
Wij (k) + ΔWij (k)
Step 8: Repeat the step 3 to step 7 until the error is minimised
to zero or there is no further chance of updating the weights
Wij.
Step 9: Stop training RBFNN.
To accomplish the best training efficiency from RBFNN, the
prerequisite is the training data and checking data (data
patterns), which are generally obtained from the simulation
results of conventional load flow using Newton-Raphson’s
method or the similar type of practical data can be obtained
from any load dispatch center. For training ATC training data
consists of real power values (Pi) as the inputs and TTC,
ATC, Convergence Status (C.S) as the outputs at various
buses. With these the RBFNN will undergo for training, once
the desired goal is reached, the trained RBFNN would
undergo for the checking of its performance with the
checking data called as test patterns. Once the results
obtained from the trained RBFNN are accurate then that
trained RBFNN will be used for computing ATC with any
new input data.
3.3 Adaptive Neuro Fuzzy Inference System (ANFIS)
The Adaptive Neuro-Fuzzy Inference System (ANFIS)
combines the concepts of fuzzy logic and neural networks to
form a hybrid intelligent system that enhances the ability to
automatically learn and adapt [16]. Hybrid systems have been
used by researchers for modelling and predictions in various
engineering systems. The basic idea behind these Neuro-
adaptive learning techniques is to provide a method for the
fuzzy modelling procedure to learn information about a data
set, in order to automatically compute the membership
function parameters that best allow the associated Fuzzy
Inference System (FIS) to track the given input/output data.
Similar to the RBFNN, the ANFIS also used to compute
ATC using a proposed method as discussed in the section IV
(C).

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4. METHODS TO COMPUTE ATC
In addition to the various methods discussed in the section I,
the following methods are used to obtain results presented in
this paper.
4.1 Method-1: ATC by Continuation Repeated
Power Flow (CRPF ATC)
In this method, the Newton Raphson’s method in polar
coordinates [17] is used to compute the TTC and ATC at
different load increments (with constant power) on the
desired load bus of different test systems. As this same
method is repeated for different load increments thus this
method is called as the repeated power flow (RPF), if the
automatic increment of loading continues till maximum
loading point is called as continuation repeated power flow
(CRPF).
4.2 Method-2: ATC using Trained RBFNN
( RBFNN ATC)
In this method, the training algorithm as discussed in the
section III (B) has been used to train the RBFNN with the
training data obtained from conventional method as discussed
in the section IV (A). The training data consisting real power
values (Pi) as the inputs and TTC, ATC, Convergence Status
(C.S) as the outputs at various buses. The RBFNN is trained
with these data patterns, once the desired goal is reached, its
performance is verified with the checking data (test patters).
The results obtained from the trained RBFNN are accurate
then it is said to be ATC computations are achieved using
RBFNN.
4.3 Method-3: ATC using Proposed MIMO ANFIS
( ANFIS ATC)
The proposed multiple input and multiple output ANFIS
(MIMO ANFIS) as shown in Fig. 1 is developed and tested
scrupulously by cascading three single input, single output
ANFIS through well-defined programming. This proposed
method will overcome the drawback of single output of
ANFIS, so that multiple outputs will be obtained from the
ANFIS. The proposed technique will become a great
advantage to the ANFIS, because ANFIS is having an ability
to automatically learn and adapt to non-linearity and the
ANFIS has been proved as the best intelligent technique in
modelling applications. Similar to method-2, the method-3
also need the same set of training data and checking data as
discussed in method-2
Fig. 1 Block diagram of proposed MIMO ANFIS
5. TEST SYSTEM AND RESULTS
5.1 Test System: Southern Region Extra High
Voltage (SREHV) 72-bus system
The latest SREHV 72-bus [18] as shown in Fig. 2 is
considered as the test system to compute ATC using various
methods as discussed in the section-IV. This test system has
divided into three areas ZONE-1, ZONE-2 and ZONE-3.
Buses 2, 3, 5, 16, 24, 25, 30, 31, 33, 35, 36, 44, 45, 54, 56, 57,
58 are in ZONE-1. Buses 1, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23,
26, 27, 32, 46, 47, 52, 55, 59, 60, 61, 62, 63, 66, 69, 70 are in
ZONE-2. Buses 9, 10, 11, 12, 13, 14, 15, 28, 29, 34, 37, 38,
39, 40, 41, 42, 43, 48, 49, 50, 51, 53, 64, 65, 67, 68, 71, 72
are in ZONE-3. Buses 1 to 15 are generator buses. Buses 16,
17, 18, 19, 20, 21, 22, 23, 25, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 48, 49, 52, 53, 54, 55, 64, 65,
66, 68, 69, 71, 72 are the load buses. The remaining buses are
used for interconnection.
5.2 Case Scenarios for Method-1:
For contingency cases, a line outage between bus-9 to bus-11
is created and is considered as contingency.
5.2.1 Case 1: Varying Load at Bus-20 without
contingency
In this case, the load at bus-20 is varied with a constant
power factor, the corresponding TTC and ATC results
obtained through method-1 are presented in Table I and the
variation between real power and ATC value can be observed
in Fig. 3.
5.2.2 Case 2: Varying Load at Bus-20 with
contingency
Similar to case-1, the corresponding TTC and ATC results
are presented in Table II and the variation between real
power and ATC value can be observed in Fig. 4.
contingency
In this case, the load at bus-34 is varied with a constant
power factor, the corresponding TTC and ATC results
obtained through method1 are presented in Table III and the
variation between real power and ATC value can be observed
in Fig. 5.
contingency
Similar to case-3, the corresponding TTC and ATC results
are presented in Table IV and the variation between real
power and ATC value can be observed in Fig. 6.

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Fig. 2 Single Line Diagram of SREHV 72-Bus Test System
5.3 Case Scenarios for Method-2 and Method-3
For contingency cases, a line outage between bus-9 to bus-11
is created and is considered as contingency.
contingency
In this case, the load at bus-20 is enhanced differently as
compared with the training data of two intelligent techniques
as discussed in the section IV (B) and IV (C). The
comparative results between method-1 vs. method-2 and
method-1 vs. method-3 are presented in the Table V and
Table IX respectively. The variation between real power and
ATC using method-1 vs. method-2 and method-1 vs. method-
3 are shown in Fig. 7 and Fig. 11 respectively.
contingency
Similar to case-1, the corresponding results obtained through
method-2 and method-3 are compared with the method-1.
The comparative results between method-1 vs. method-2 and
method-1 vs. method-3 are presented in the Table VI and
Table X respectively. The variation between real power and
contingency
In this case, the load at bus-34 is enhanced differently as
compared with the training data of two intelligent techniques
as discussed in the section IV (B) and IV (C). The
comparative results between method-1 vs. method-2 and
method-1 vs. method-3 are presented in the Table VII and
Table XI respectively. The variation between real power and
contingency
Similar to case-3, the corresponding results obtained through
method-2 and method-3 are compared with the method-1.
The comparative results between method-1 vs. method-2 and
method-1 vs. method-3 are presented in the Table VIII and
Table XII respectively. The variation between real power and

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5.4 Results
The TRM and CBM used in the equation (1) are often
represents to the existing transmission commitments. In the
power flow studies this existing transmission commitments
are usually represented as base load at particular bus. Thus in
this paper, the ATC is computed as ATC=TTC- Base Load.
The MATPOWER 4.1open software package [19] and the
MATLAB [20]programming environment has been used to
obtain the results for different case scenarios.
5.4.1 Method-1 CRPF ATC Data for SREHV 72-Bus System
Table 1 ATC Data for variation of load at bus-20 Table 2 ATC Data for variation of load at bus-20
without contingency with contingency
Table 3 ATC Data for variation of load at bus-34 Table 4 ATC Data for variation of load at bus-34
without contingency with contingency
5.4.2 Method-1 CRPF ATC Figures for SREHV 72-Bus System
0 200 400 600 800
0
200
400
600
800
ATC At Bus-20 (MW)
VaryingLoadatBus-20(MW)
ATC at Bus-20 Without Contingency
X = 746
Y = 808
0 200 400 600 800
0
200
400
600
800
ATC At Bus-20 (MW)
ATC at Bus-20 With Contingency
X = 741
Y = 803
Fig. 3 ATC Bar plot for increasing Load at Bus-20 Fig. 4 ATC Bar plot for increasing Load at Bus-20
without Contingency with Contingency

_______________________________________________________________________________________
Fig. 5 ATC Area plot for increasing Load at Bus-34 Fig. 6 ATC Area plot for increasing Load at Bus-34
without Contingency with Contingency
5.4.3 Method-2 RBFNN ATC Data for SREHV 72-Bus System
Table 5 Data for variation of load at bus-20 without contingency
Test Patterns
(Checking Data)
Real Power
P20 (MW)
CRPF METHOD RBFNN METHOD
% ATC Error
TTC2
( MW)
ATC2
(MW)
C.S
TTC
( MW)
ATC
(MW)
C.S
1 105 2250.42 42.8 1 2250.421 42.80105 1 -0.00245
2 280 2425.42 217.8 1 2425.42 217.8 1 0.000
3 455 2600.42 392.8 1 2600.42 392.8 1 0.000
4 630 2775.42 567.8 1 2775.42 567.8 1 0.000
5 805 2950.42 742.8 1 2950.42 742.8 1 0.000
6 808.7 2954.12 746.5 0 2954.12 746.5 0 0.000
3-
10x54.2100x
801055.42
42.80105-42.8
100x
ATCObtained
ATCObtained-ATCReference
ErrorATC% 
Table 6 Data for variation of load at bus-20 with contingency
Test Patterns
(Checking Data)
Real Power
P20 (MW)
% ATC Error
TTC2
( MW)
ATC2
(MW)
C.S
TTC
( MW)
ATC
(MW)
C.S
1 105 2250.42 42.8 1 2250.421 42.80109 1 -0.00255
2 630 2775.42 567.8 1 2775.42 567.8 1 0.000
3 787 2932.42 724.8 1 2932.42 724.8 1 0.000
4 803 2948.42 740.8 1 2948.42 740.8 1 0.000
5 803.2 2948.62 741 1 2948.62 741 1 0.000
6 803.5 2948.92 741.5 0 2948.92 741.5 0 0.000

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Test Patterns
(Checking Data)
Real Power
P34 (MW)
% ATC Error
TTC3
( MW)
ATC3
(MW)
C.S
TTC
( MW)
ATC
(MW)
C.S
1 385 3297.69 39.39 1 3297.691 39.39104 1 -0.00264
2 560 3472.69 214.39 1 3472.69 214.39 1 0.000
3 700 3612.69 354.39 1 3612.69 354.39 1 0.000
4 840 3752.69 494.39 1 3752.69 494.39 1 0.000
5 859 3771.69 513.39 1 3771.69 513.39 1 0.000
6 860 3772.69 514.39 0 3772.69 514.39 0 0.000
Test Patterns
(Checking Data)
Real Power
P34 (MW)
% ATC Error
TTC3
( MW)
ATC3
(MW)
C.S
TTC
( MW)
ATC
(MW)
C.S
1 385 3297.69 39.39 1 3297.691 39.39102 1 -0.00259
2 560 3472.69 214.39 1 3472.69 214.39 1 0.000
3 770 3682.69 424.39 1 3682.69 424.39 1 0.000
4 857.1 3769.79 511.49 1 3769.79 511.49 1 0.000
5 857.6 3770.29 511.99 1 3770.29 511.99 1 0.000
6 857.7 3770.39 512.09 0 3770.39 512.09 0 0.000
5.4.4 Method-2 RBFNN ATC Figures for SREHV 72-Bus System
0 200 400 600
0
200
400
600
800
ATC at Bus-20 (MW)
CRPF and RBFNN ATC Comparision Curve at Bus-20
With Contingency
CRPF
RBFNN
0 200 400 600
0
200
400
600
800
ATC at Bus-20 (MW)
Without Contingency
CRPF
RBFNN
Fig. 7 CRPF and RBFNN ATC Plot for increasing Fig. 8 CRPF and RBFNN ATC Plot for increasing
Load at Bus-20 without Contingency Load at Bus-20 with Contingency

_______________________________________________________________________________________
0 100 200 300 400 500
0
200
400
600
800
ATC at Bus-34 (MW)
Without Contingency
CRPF
RBFNN
0 100 200 300 400 500
0
200
400
600
800
ATC at Bus-34 (MW)
With Contingency
CRPF
RBFNN
Fig. 9 CRPF and RBFNN ATC Plot for increasing Fig. 10 CRPF and RBFNN ATC Plot for increasing
5.4.5 Method-3 ANFIS ATC Data for SREHV 72-Bus System
Test Patterns
(Checking Data)
Real Power
P20 (MW)
CRPF METHOD ANFIS METHOD
% ATC Error
TTC2
( MW)
ATC2
(MW)
C.S
TTC
( MW)
ATC
(MW)
C.S
1 105 2250.42 42.8 1 2250.42 42.80003 1 0.000
2 280 2425.42 217.8 1 2425.42 217.8 1 0.000
3 455 2600.42 392.8 1 2600.419 392.8 1 0.000
4 630 2775.42 567.8 1 2775.421 567.8 1 0.000
5 805 2950.42 742.8 1 2950.42 742.8 1 0.000
6 808.7 2954.12 746.5 0 2954.12 746.5 0 0.000
Test Patterns
(Checking Data)
Real Power
P20 (MW)
% ATC Error
TTC2
( MW)
ATC2
(MW)
C.S
TTC
( MW)
ATC
(MW)
C.S
1 105 2250.42 42.8 1 2250.42 42.80004 1 0.000
2 630 2775.42 567.8 1 2775.421 567.8 1 0.000
3 787 2932.42 724.8 1 2932.42 724.8 1 0.000
4 803 2948.42 740.8 1 2948.419 740.8 1 0.000
5 803.2 2948.62 741 1 2948.619 741 1 0.000
6 803.5 2948.92 741.5 0 2948.92 741.5 0 0.000

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Test Patterns
(Checking Data)
Real Power
P34 (MW)
% ATC Error
TTC3
( MW)
ATC3
(MW)
C.S
TTC
( MW)
ATC
(MW)
C.S
1 385 3297.69 39.39 1 3297.688 39.39034 1 0.000
2 560 3472.69 214.39 1 3472.692 214.3897 1 0.000
3 700 3612.69 354.39 1 3612.687 354.3904 1 0.000
4 840 3752.69 494.39 1 3752.69 494.3899 1 0.000
5 859 3771.69 513.39 1 3771.691 513.3899 1 0.000
6 860 3772.69 514.39 0 3772.69 514.39 0 0.000
Test Patterns
(Checking Data)
Real Power
P34 (MW)
% ATC Error
TTC3
( MW)
ATC3
(MW)
C.S
TTC
( MW)
ATC
(MW)
C.S
1 385 3297.69 39.39 1 3297.689 39.39021 1 0.000
2 560 3472.69 214.39 1 3472.689 214.39 1 0.000
3 770 3682.69 424.39 1 3682.692 424.3897 1 0.000
4 857.1 3769.79 511.49 1 3769.789 511.4901 1 0.000
5 857.6 3770.29 511.99 1 3770.289 511.9901 1 0.000
6 857.7 3770.39 512.09 0 3770.39 512.09 0 0.000
5.4.6 Method-3 ANFIS ATC Figures for SREHV 72-Bus System
0 200 400 600
0
200
400
600
800
ATC at Bus-20 (MW)
CRPF and ANFIS ATC Comparision Curve at Bus-20
Without Contingency
CRPF
ANFIS
0 200 400 600
0
200
400
600
800
ATC at Bus-20 (MW)
With Contingency
CRPF
ANFIS
Fig. 11 CRPF and ANFIS ATC Plot for increasing Fig. 12 CRPF and ANFIS ATC Plot for increasing

_______________________________________________________________________________________
0 100 200 300 400 500
0
200
400
600
800
ATC at Bus-34 (MW)
Without Contingency
CRPF
ANFIS
0 100 200 300 400 500
0
200
400
600
800
ATC at Bus-34 (MW)
With Contingency
CRPF
ANFIS
Fig. 13 CRPF and ANFIS ATC Plot for increasing Fig. 14 CRPF and ANFIS ATC Plot for increasing
6. CONCLUSION
In this paper, the latest Indian southern region extra high
voltage 72-bus system used to compute the ATC at the
desired buses starting from the base loading point to till the
maximum loading point. The ATC obtained at this
maximum loading point is called as voltage stability
constrained ATC (VSC ATC), this ATC is maximum ATC
at the desired buses and it has been marked as bold in the
different tables and figures presented in this paper. From
these results it has observed that the amount of VSC ATC is
less for the contingency cases. The results obtained from the
continuation repeated power flow are used to train the radial
basis function neural network (RBFNN) and proposed
MIMO adaptive neuro fuzzy inference system (MIMO
ANFIS). After satisfied training of RBFNN and MIMO
ANFIS, the performance of these intelligent techniques are
verified with respect to different test patterns. These test
patterns and comparative results indicating that the results
obtained through the RBFNN and proposed MIMO ANFIS
are accurate because the percentage of error is almost zero.
It has proved in this paper with the number of case scenarios.
Hence for computing ATC in the case of on-line
environment the RBFNN and proposed ANFIS can be used
due to their superior advantages over the conventional
methods. From the results it has been proved that the level
of accuracy is more in the case of proposed MIMO ANFIS
when compared with the RBFNN and also the ANFIS will
have the superior system modelling features as compared
with RBFNN. During the open power markets the
computation of ATC will becoming a significant feature and
its value is a dynamic phenomenon, in this type of situations
till depending on steady state computations would not
provide precise ATC values. Therefore this paper suggesting
the proposed the MIMO ANFIS will be the best for
computing ATC for the different power markets as
compared with the other existing methods.
REFERENCES
[1] Ejebe, Gabriel C., et al. "Available transfer
capability calculations." IEEE Transactions
on Power Systems, 13.4 (1998): 1521-1527.
[2] Ou, Yan, and Chanan Singh. "Calculation of risk and
statistical indices associated with available transfer
capability." IEE Proceedings-Generation,
Transmission and Distribution 150.2 (2003): 239-
244.
[3] Ou, Yan, and Chanan Singh. "Assessment of
available transfer capability and margins." Power
Systems, IEEE Transactions on 17.2 (2002): 463-
468.
[4] Sauer, Peter W. "Technical challenges of computing
available transfer capability (ATC) in electric power
systems." System Sciences, 1997, Proceedings of the
Thirtieth Hawaii International Conference on. Vol. 5.
IEEE, 1997.
[5] Gravener, Mark H., and Chika Nwankpa. "Available
transfer capability and first order sensitivity." IEEE
Transactions on Power Systems, 14.2 (1999): 512-
518.
[6] Ejebe, Gabriel C., et al. "Fast calculation of linear
available transfer capability." IEEE Transactions
on Power Systems, 15.3 (2000): 1112-1116.
[7] Mozafari, B., et al. "A comprehensive method for
available transfer capability calculation in a
deregulated power." Electric Utility Deregulation,
Restructuring and Power Technologies, 2004.(DRPT
2004). Proceedings of the 2004 IEEE International
Conference on. Vol. 2. IEEE, 2004.
[8] Grijalva, Santiago, Peter W. Sauer, and James D.
Weber. "Enhancement of linear ATC calculations by
the incorporation of reactive power flows." Power
Systems, IEEE Transactions on 18.2 (2003): 619-
624.
[9] Kumar, DM Vinod, G. Narayan Reddy, and Ch
Venkaiah. "Available transfer capability (ATC)
determination using intelligent techniques." Power
India Conference, 2006 IEEE. IEEE, 2006.

_______________________________________________________________________________________
[10] P. Gopi Krishna, T. Gowri Manohar, and G. N.
Srinivas. "Online monitoring of available transfer
capability in deregulated power system using
Adaptive Neuro Fuzzy Inference System." Power
Electronics and Instrumentation Engineering.
Springer Berlin Heidelberg, 2010. 74-79.
[11] Zhang, Xuemin, et al. "Dynamic available transfer
capability (ATC) evaluation by dynamic constrained
optimization." Power Systems, IEEE Transactions
on19.2 (2004): 1240-1242..
[12] Warwick, Kevin, Arthur Ekwue, and Raj Aggarwal,
eds. Artificial intelligence techniques in power
systems. vol . 22, 1997.
[13] “Available Transfer Capability Definitions and
Determination: A Reference Document prepared by
TTC Task Force”, North American Electric
Reliability Council (NERC), June 1996.
[14] Zurada, Jacek M. Introduction to artificial neural
systems. St. Paul: West publishing company, Vol. 8 ,
1992.
[15] White, Halbert. Artificial neural networks:
approximation and learning theory. Blackwell
Publishers, Inc., 1992.
[16] Kasabov, Nikola, “K. Foundations of neural
networks, fuzzy systems, and knowledge
engineering”. Marcel Alencar, 1996.
[17] Powell, Lynn. Power System Load Flow Analysis
(Professional Engineering). McGraw-Hill
Professional, 2004.
[18] Thukaram, D., K. Parthasarathy, et.al "Voltage
stability improvement: case studies of Indian power
networks." Electric power systems research, Elsevier
Issue no. 1, pp. 35-44, 1998.
[19] Matpower package
www.pserc.cornell.edu/matpower4.0
[20] www.mathworks.com/help/toolbox/nnet(or, fuzzy)
BIOGRAPHIES
Mr. P. GopiKrishna is pursuing Ph.D
in the Department of Electrical and
Electronics Engineering at Sri
Venkateshwara University, Tirupathi,
India and he is working as a Lecturer at
Ibra College of Technology, Ibra,
OMAN. He has 15 years of teaching experience in
Electrical and Electronics Engineering and since 10 years he
has been involving in research, related to the application of
intelligent techniques like ANN, Fuzzy Logic and ANFIS to
solve different problems of electrical power systems. His
research interests are Voltage Stability, Available Transfer
Capability, Neural Networks, Fuzzy Logic Systems,
Adaptive Neuro Fuzzy Inference Systems, Cognitive
Systems, Data Acquisition and Support Vector Machines.
Dr. T. Gowri Manohar received the
B.Tech, M.Tech and Ph.D Degrees in
Electrical and Electronics Engineering
from the S.V.University, Tirupati, India.
Presently he is working as an Associate
Professor in the department of Electrical
and Electronics Engineering S.V.University, Tirupati, India.
He is having 18 years of teaching experience and he has
published several papers in the various national,
international conferences and journals. He is a senior
Member of IEEE, MIE and LMISTE. His research areas of
interests are Modern Restructured Power Systems, Electrical
Drives, Power Quality, harmonics issues and challenges.

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