prediction of transmission line overloading using

8/2/2019 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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1) Savita Sharma, Laxmi Srivastava, Prediction of transmission line overloading

using intelligent technique, Applied Soft Computing 8 (2008) pp-626633.(Base

Paper)

2) Shandilya, H. Gupta, J. Sharma, A method for generation rescheduling and load

shedding to alleviate line overloads using local optimisation, IEE Proceedings

on Generation, Transmission and Distribution 140 (5) (1993) pp-337342.

3) S. Wei, V. Vittal, Corrective switching algorithm for relieving overloads and

voltage violations, IEEE Transactions on Power Systems 20 (4) (2005) pp-1877

1885.

4) I. Hiskens, B. Gong, MPC-based load shedding for voltage stability

enhancement, in: Proceeding of the 44th IEEE Conference on Decision and

Control, Seville, Spain, December 1215, (2005), pp-44634468.


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5) N. Kumar, R. Wangneo, P.K. Kalra, S.C. Srivastava, Application of artificial neural

network to load flow, in Proceedings TENCON91, IEE Region 10 International

Conference on EC3-Energy, Computer, Communication and Control System, vol.

1, 1991, pp-199203.

6) Daya Ram, Laxmi Srivastava, Manjaree Pandit, Jaydev Sharma, Corrective action

planning using RBF neural network, Applied Soft Computing 7 (2007) pp-1055

1063.

7) Y.Y. Hsu, C.R. Chen, C.C. Su, Analysis of electromechanical modes using an

artificial neural network, IEE Proceedings Generation, Transmission and

Distribution 141 (1994) pp-198204.

8) A.N. Adupa, G.K. Purushothama, K. Parthasarathy, D. Thukaram, A fuzzy control

for network overload alleviation, International Journal of Electrical Power &

Energy Systems 23 (2) (2001) pp-119128.


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9) R. Anand, Kishan G. Mehrotra, C.K. Mohan, S. Ranka, An improved algorithm

for neural network classification of imbalanced training sets, IEEE Trans.

Neural Networks 4 (1993) pp-962969.

10) K.L. Ho, Y.Y. Hsu, C. Yang, Short term load forecasting using a multilayer

neural network with an adaptive learning algorithm, IEEE Trans. Power

Systems 7 (1992) pp-141149.

11) K.L. Lo, L.J.Peng, J.F. Macqueen, A.O. Ekwue, D.T.Y. Cheng, Fast real power

contingency ranking using counter propagation neural network, IEEE Trans.

Power Systems 13 (1998) pp-12591264.

12) L.L. Freris, A.M. Sasson, Investigation of the load-flow problem, Proc. IEE 115

(10) (1968) pp-14591470.

13) P.A. Chamorel, A.J. Germond, An efficient constrained power flow technique

based on active reactive decoupling and the use of linear programming, IEEE

Transactions on Power Apparatus and System 101 (1982) pp-158167.


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