prediction of transmission line overloading using
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A
SEMINAR PRESENTATION
ON
PREDICTION OF TRANSMISSION LINE OVERLOADING USING
INTELLIGENT TECHNIQUE
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Introduction
Artificial neural networks
Methodology
Line overloading calculations
Input feature selection
Simulation results
Conclusions
References
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Need of Prediction of overloading in transmission lines
The enormous expansion of Generation system.
Transmission lines are overloaded and that results in high
losses.
The cases of voltage limit violation and line loading limit
violation are increasing day by day.
The violations can be more severe in case of any contingencies.
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It is the functional imitation of a human brain .
It consists of neurons.
It involves into two phases- training or learning phase and testing
phase.
Role of ANN in this research
Here identification and prediction of transmission line overloading is
done.
A cascade neural network based approach is proposed for it.
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All the training patterns are applied to the identification module.
The identified overloaded cases are then passed to the prediction
module.
The input features are taken from the set of real power injections at
generation (PV) and load (PQ) buses.
After that Input selection is done. (explained later)
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Large number of load patterns generated by
random load variation
Full AC power flow run to compute real power
flows
Selection of INPUT features using angular based
clustering technique
Normalization of input data between 0.9 and 0.1
Training the CNN using the normalized data
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Line overloading = real power flow in the line - rating of the line.
Done using Newton-Raphson power flow method.
The objective is to determine the voltage and its angle at each bus, real
and reactive power flows in each line and line losses.
Variables associated with each bus of a power system include four
quantities viz. voltage magnitude Vi, its phase angle , real power Piand reactive power
4m variables for m bus system.
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From the nodal current equations, the total current entering the ith bus of m bus
system is given by
=
Where is the admittance of the line between buses iand kand is thevoltage at bus k. In polar coordinates
At ith bus, complex conjugate power will be
(
=)
On solving this equation the real power and the reactive power can be obtained.
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The real power at ith bus will be
[
=(++)]
Or
=. [ ]
And the reactive power at ith bus will be
.[
=(++)]
Or
= . [ ]
The above equations are known as static load flow equations(SLPE).
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The power flow equations used in Newton-Raphson method for
computation of voltage corrections are given as
where, H, N, Jand L are the sub-matrices of the Jacobian .
The ikth matrices ofH,N,Jand L are,
;
;
The solutions provide the correction vector i. e. for all the PVand PQtype buses and s for all the PQ type buses which are used to update theearlier estimates ofs and Vs. This iterative process is continued till the
convergence is obtained.
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Once the solution of bus voltages ( and for load buses and for generationbuses) is found, the power flows in line between buses iand k can be calculated
using nominal-pi representation of the line. Current flow from bus itowards bus k
will be
The power flow in the line i-kat the bus iis given by
Here is line charging of the line between buses iand k.
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To decrease size and the number of interconnections in the neural
network input feature selection is done
Feature selection methods like entropy reduction method, principal
component method, correlation coefficient based method, angular
distance based method etc. are available in the literature.
Here we group the total M system variables (SV1, SV2, . . ., SVM)
into G clusters such that the variables in a cluster have similar
characteristics.
One representative variable from each cluster is picked out as a
feature for the cluster. Thus the number of variables will be reduced
from M to G.
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Test system- IEEE-14 Bus system.
Changing the load at each bus and generation at PV buses randomly
several load patterns were generated and the power factor at each
load is maintained constant.
The NR power flow method was used to compute power flows for
each loading scenario.
The input as well as output data were normalised between 0.9 and
0.1.
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120 load scenarios generated by changing the load at each bus and
generation randomly in wide range.
Total load scenarios = 2400
Patterns used for training = 1600
Patterns used for validation=400
Patterns used for testing= 400
The proposed identification module ANN1 (12-27-1).
ANN1 is trained using a BP algorithm.
The Prediction module ANN2 (12-85-1)
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Line No. From bus To bus Target T ANN O Class
1 1 2 0.9 0.8949 OL
2 1 8 0.1 0.1971 UL
3 2 3 0.9 0.8957 OL
4 2 6 0.9 0.8773 OL
5 2 8 0.9 0.8585 OL
6 3 6 0.1 0.1015 UL
7 4 11 0.1 0.1042 UL
8 4 12 0.1 0.1089 UL
9 4 13 0.1 0.1049 UL
10 6 7 0.9 0.8430 OL
11 6 8 0.1 0.1033 UL
12 6 9 0.1 0.1016 UL
13 7 5 0.1 0.1011 UL
14 7 9 0.1 0.1023 UL
15 8 4 0.9 0.8645 OL
16 9 10 0.1 0.1017 UL
17 9 14 0.1 0.1034 UL
18 10 11 0.1 0.1043 UL
19 12 13 0.1 0.10407 UL
20 13 14 0.1 0.1146 UL
IDENTIFICATION OF OVERLOADING FOR IEEE 14-BUS SYSTEM
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Total training patterns- 1600
Overloading cases- 586
During testing 400 unseen cases are applied to the trained module.
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Line No. From bus To bus Target ANN Error(pu)
1 1 2 0.6157 0.6163 -0.0006
3 2 3 0.2695 0.2712 -0.0017
4 2 6 0.3886 0.3898 -0.0012
5 2 8 0.3408 0.3391 -0.0017
10 6 7 0.1714 0.1737 -0.0023
15 8 4 0.2313 0.2352 -0.0039
AMOUNT OF OVERLOADING
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Identification and prediction of overloading essential.
Analytical methods take a long time as ac power flow analysis has to
be carried out for any change in loading/generation condition.
But with CNN the identification and prediction can be instantaneous.
After prediction, a control action can be taken.
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