A Fuzzy Logic System to Detect and Classify Faults for Laboratory Prototype Model of TCSC Compensated Transmission Line
Abstract
In this paper, an expert system-based fault detection and classification scheme is developed for a laboratory prototype modelof TCSC compensated long transmission line (thyristor controlled series compensator). The equivalent model of laboratoryprototype system is simulated in MATLAB Simulink. An expert system based on fuzzy logic is developed by using threephasevoltage and current signals from single end measurements. Obtained voltage and current signals are pre-processedwith Discrete Fourier Transform (DFT) to obtain the fundamental component of these signals. Further zero sequence currentand obtained fundamental voltage and current signals are used to develop a fuzzy inference system (FIS) for shunt faultdetection and classification task. There are three different FISs developed for three individual phases of the transmissionsystem and one FIS is developed for zero sequence current signal, which provides ground involvement information. Thecombined binary output of the developed four FISs provides fault classification. The performance of the developed FISs isrigorously tested with the variation of different fault parameters, and different location of the TCSC. The simulated resultsindicate that the proposed scheme performance is reliable in its zone of protection.References
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of flexible AC transmission systems, IEEE press, 2000.
[2] P. Dash, A. Pradhan, G. Panda, Apparent impedance calculations
for distance-protected transmission lines employing series-connected
facts devices, Electric Power Components and Systems 29 (7) (2001)
577–595.
[3] S. Jamali, A. Kazemi, H. Shateri, Distance relay over-reaching due to
installation of tcsc on next line, in: 2006 IEEE International Symposium
on Industrial Electronics, Vol. 3, IEEE, 2006, pp. 1954–1959.
[4] P. Dash, A. Pradhan, G. Panda, A. Liew, Digital protection of power
transmission lines in the presence of series connected facts devices,
in: 2000 IEEE Power Engineering Society Winter Meeting. Conference
Proceedings (Cat. No. 00CH37077), Vol. 3, IEEE, 2000, pp. 1967–
1972.
[5] B. Vyas, R. P. Maheshwari, B. Das, Protection of series compensated
transmission line: issues and state of art, Electric power systems research
107 (2014) 93–108.
[6] B. Kumar, A. Yadav, Backup protection scheme for transmission line
compensated with upfc during high impedance faults and dynamic situations,
IET Science, Measurement & Technology 11 (6) (2017) 703–
712.
[7] B. Kumar, A. Yadav, A. Y. Abdelaziz, Synchrophasors assisted protection
scheme for the shunt-compensated transmission line, IET Generation,
Transmission & Distribution 11 (13) (2017) 3406–3416.
[8] H. Wang, W. Keerthipala, Fuzzy-neuro approach to fault classification
for transmission line protection, IEEE Transactions on Power Delivery
13 (4) (1998) 1093–1104.
[9] A. Yadav, A. Thoke, Transmission line fault distance and direction estimation
using artificial neural network, International Journal of Engineering,
Science and Technology 3 (8) (2011) 110–121.
[10] B. K. Chaitanya, A. K. Soni, A. Yadav, Communication assisted fuzzy
based adaptive protective relaying scheme for microgrid, Journal of
Power Technologies 98 (1) (2018) 57–69.
[11] B. Y. Vyas, R. Maheshwari, B. Das, Improved fault analysis technique
for protection of thyristor controlled series compensated transmission
line, International Journal of Electrical Power & Energy Systems 55
(2014) 321–330.
[12] P. Dash, M. Chilukuri, Soft computing tools for protection of compensated
network, in: Proceedings. National Power Engineering Conference,
2003. PECon 2003., IEEE, 2003, pp. 52–61.
[13] A. Pradhan, A. Routray, S. Pati, D. Pradhan, Wavelet fuzzy combined
approach for fault classification of a series-compensated transmission
line, IEEE Transactions on Power Delivery 19 (4) (2004) 1612–1618.
[14] P. Dash, S. Samantaray, G. Panda, Fault classification and section
identification of an advanced series-compensated transmission line
using support vector machine, IEEE transactions on power delivery
22 (1) (2007) 67–73.
[15] U. B. Parikh, B. Das, R. Maheshwari, Fault classification technique for
series compensated transmission line using support vector machine,
International Journal of Electrical Power & Energy Systems 32 (6)
(2010) 629–636.
[16] P. Tripathi, G. Pillai, H. Gupta, New method for fault classification in
tcsc compensated transmission line using ga tuned svm, in: 2012
IEEE International Conference on Power System Technology (POWERCON),
IEEE, 2012, pp. 1–6.
[17] B. Vyas, R. P. Maheshwari, B. Das, Evaluation of artificial intelligence
techniques for fault type identification in advanced series compensated
transmission lines, IETE Journal of Research 60 (1) (2014) 85–91.
[18] B. Bahmanifirouzi, E. Farjah, T. Niknam, E. A. Farsani, A new hybrid
hbmo-sfla algorithm for multi-objective distribution feeder reconfiguration
problem considering distributed generator units, Iranian Journal
of Science and Technology. Transactions of Electrical Engineering
36 (E1) (2012) 51.
[19] A. Swetapadma, A. Yadav, Improved fault location algorithm for multilocation
faults, transforming faults and shunt faults in thyristor controlled
series capacitor compensated transmission line, IET Generation,
Transmission & Distribution 9 (13) (2015) 1597–1607.
[20] G. R. Rajeswary, G. R. Kumar, G. J. S. Lakshmi, G. Anusha, Fuzzywavelet
based transmission line protection scheme in the presence of
tcsc, in: 2016 International Conference on Electrical, Electronics, and
Optimization Techniques (ICEEOT), IEEE, 2016, pp. 4086–4091.
[21] S. Cai, G. Liu, Study on application of fisher information for power
system fault detection, Journal of Power Technologies 98 (3) (2016)
274–280.
[22] SimPower Systems Toolbox ver. 8.1, for use with Simulink, User’s
Guide. The Math Works.
Published
2019-04-04
How to Cite
KUMAR, Bhupendra; YADAV, Anamika.
A Fuzzy Logic System to Detect and Classify Faults for Laboratory Prototype Model of TCSC Compensated Transmission Line.
Journal of Power Technologies, [S.l.], v. 99, n. 1, p. 49–57, apr. 2019.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1290>. Date accessed: 20 apr. 2024.
Issue
Section
Electrical Engineering
Keywords
FACTS; TCSC; Fuzzy Logic; DFT; Fault Detection; Fault classification; Power transmission
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