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International Journal of Fuzzy Logic and Intelligent Systems 2021; 21(4): 391-400

Published online December 25, 2021

https://doi.org/10.5391/IJFIS.2021.21.4.391

© The Korean Institute of Intelligent Systems

Fuzzy Rule-Based Fault Location Technique for Thyristor-Controlled Series-Compensated Transmission Lines

A. Naresh Kumar1, M. Ramesha2, S. Jagadha3, Bharathi Gururaj4, M. Suresh Kumar5, and Kommera Chaitanya6

1Department of Electrical and Electronics Engineering, Institute of Aeronautical Engineering, Hyderabad, India
2Department of Electrical, Electronics and Communication Engineering, GITAM (Deemed to be University), Bengaluru, India
3Department of Mathematics, Institute of Aeronautical Engineering, Hyderabad, India
4Department of Electronics and Communication Engineering, ACS College of Engineering, Bengaluru, India
5Department of Aerospace Engineering, Sandip University, Nashik, India
6Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Proddatur, India

Correspondence to :
A. Naresh Kumar (ankamnaresh29@gmail.com)

Received: June 17, 2021; Revised: August 21, 2021; Accepted: September 9, 2021

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Estimating the distance of a transmission line with a flexible alternating current transmission system including a thyristor-controlled series compensator is a challenging task. The distance estimation technique based on a fuzzy rule-based system (FRS) in a thyristor-controlled seriescompensated transmission line with multi-location faults is investigated in this study. The Haar wavelet current coefficients of the relaying bus are utilized as inputs to accomplish the distance estimation task. The FRS is illustrated through the Mamdani system in the LabVIEW software. The efficacy of the FRS is studied considering the effects of variation with respect to fault parameters. The main characteristic FRS is that it does not involve any two-end communication links because it employs relay terminal measurements only.

Keywords: Fuzzy rule-based system, Multi-location faults, Transmission lines

No potential conflicts of interest relevant to this article was reported.

A. Naresh Kumar received the B.Tech. degree in Electrical and Electronics Engineering from Jawaharlal Nehru Technological University, Jagitiala, India in 2011. He received his M.Tech. degree in Electrical Engineering from National Institute of Technology, Raipur, India, in 2013. He completed his Ph.D. from department of Electrical and Electronics Engineering, GITAM University, Hyderabad. His areas of interest are power system protection, smart grid and soft computing applications.

E-mail: ankamnaresh29@gmail.com


M. Ramesha received his Bachelor’s degree from the VTU, Belagavi, and M.Tech. from Dayananda Sagar College of Engineering, Bangalore, and his Ph.D. degree from the GITAM University, Visakhapatnam. From January 2010 to June 2013, he was employed as Lecturer in YDIT, Bengaluru, and from June 2013 to July 2019, he worked as an Assistant Professor at ACS College of Engineering, Bengaluru. His research interests are in the area of the next-generation wireless communication system with special emphasis on various 5G technologies such as massive MIMO, mm-Wave, OFDM, FBMC, NOMA, and others.

E-mail: ameshmalur037@gmail.com


S. Jagadha received the B.Sc. degree in Mathematics from University of Madras, India in 1989. She received her M.Sc. degree in Mathematics from Osmania University, Hyderabad, India, in 2002. She completed her Ph.D. from department of Mathematics, Jawaharlal Nehru Technological University Anantapur.

E-mail: jagadhasaravanan@gmail.com


Bharathi Gururaj received her bachelor’s degree from the PES Institute of Technology, Bangalore University, and M.Tech. from M S Ramaiah Institute of Technology, Bangalore, and Ph.D. degrees from the VTU, Belagavi. From July 2008 to June 2012, she was employed as Assistant Professor in SVCE, Bengaluru, and from July 2012 to Till date, she worked as an Assistant & Associate Professor at ACS College of Engineering, Bengaluru. Her research interests are in the image & video processing, wireless communication system. She has contributed more than 8 research journals.

E-mail: bharathigururaj@gmail.com


M. Suresh Kumar is holding 15 years experience, he is working as Prof. in the Dept. of Aerospace Engineering, Sandip University, Nashik. His research interests include: Electrical and Avionics. He has multiple publications to his credits.

E-mail: hodaerospace@sandipuniversity.edu.in


Kommera Chaitanya received the B.Tech. degree in electrical and electronics engineering from Vaagdevi Institute of Technology and Science, Proddatur, India in 2008. He received his M.Tech. degree in Electrical Engineering from Mahaveer institute of science and technology, Hyderabad, India, in 2011. His areas of interest are power system protection, smart grid and soft computing applications.

E-mail: chaitanya.k407@gmail.com


Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2021; 21(4): 391-400

Published online December 25, 2021 https://doi.org/10.5391/IJFIS.2021.21.4.391

Copyright © The Korean Institute of Intelligent Systems.

Fuzzy Rule-Based Fault Location Technique for Thyristor-Controlled Series-Compensated Transmission Lines

A. Naresh Kumar1, M. Ramesha2, S. Jagadha3, Bharathi Gururaj4, M. Suresh Kumar5, and Kommera Chaitanya6

1Department of Electrical and Electronics Engineering, Institute of Aeronautical Engineering, Hyderabad, India
2Department of Electrical, Electronics and Communication Engineering, GITAM (Deemed to be University), Bengaluru, India
3Department of Mathematics, Institute of Aeronautical Engineering, Hyderabad, India
4Department of Electronics and Communication Engineering, ACS College of Engineering, Bengaluru, India
5Department of Aerospace Engineering, Sandip University, Nashik, India
6Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Proddatur, India

Correspondence to:A. Naresh Kumar (ankamnaresh29@gmail.com)

Received: June 17, 2021; Revised: August 21, 2021; Accepted: September 9, 2021

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Estimating the distance of a transmission line with a flexible alternating current transmission system including a thyristor-controlled series compensator is a challenging task. The distance estimation technique based on a fuzzy rule-based system (FRS) in a thyristor-controlled seriescompensated transmission line with multi-location faults is investigated in this study. The Haar wavelet current coefficients of the relaying bus are utilized as inputs to accomplish the distance estimation task. The FRS is illustrated through the Mamdani system in the LabVIEW software. The efficacy of the FRS is studied considering the effects of variation with respect to fault parameters. The main characteristic FRS is that it does not involve any two-end communication links because it employs relay terminal measurements only.

Keywords: Fuzzy rule-based system, Multi-location faults, Transmission lines

Fig 1.

Figure 1.

TCSCTL connection diagram.

The International Journal of Fuzzy Logic and Intelligent Systems 2021; 21: 391-400https://doi.org/10.5391/IJFIS.2021.21.4.391

Fig 2.

Figure 2.

Flow chart of proposed FRS framework.

The International Journal of Fuzzy Logic and Intelligent Systems 2021; 21: 391-400https://doi.org/10.5391/IJFIS.2021.21.4.391

Fig 3.

Figure 3.

Current coefficients during multi-location fault.

The International Journal of Fuzzy Logic and Intelligent Systems 2021; 21: 391-400https://doi.org/10.5391/IJFIS.2021.21.4.391

Fig 4.

Figure 4.

Fuzzy rule-based system.

The International Journal of Fuzzy Logic and Intelligent Systems 2021; 21: 391-400https://doi.org/10.5391/IJFIS.2021.21.4.391

Fig 5.

Figure 5.

The input, outputs, and their membership functions.

The International Journal of Fuzzy Logic and Intelligent Systems 2021; 21: 391-400https://doi.org/10.5391/IJFIS.2021.21.4.391

Fig 6.

Figure 6.

IF-THEN rules.

The International Journal of Fuzzy Logic and Intelligent Systems 2021; 21: 391-400https://doi.org/10.5391/IJFIS.2021.21.4.391

Fig 7.

Figure 7.

The outputs of FRS where phase B is located at 50 km at 46 ms and C is located at 14 km at 46 ms, with the other output A located at 100 km, indicating that there exists a BC multi-location fault.

The International Journal of Fuzzy Logic and Intelligent Systems 2021; 21: 391-400https://doi.org/10.5391/IJFIS.2021.21.4.391

Fig 8.

Figure 8.

The outputs of FRS where phase A is located at 83 km at 46 ms, with other outputs B and C located at 100 km, indicating that there exists a BC-shunt fault.

The International Journal of Fuzzy Logic and Intelligent Systems 2021; 21: 391-400https://doi.org/10.5391/IJFIS.2021.21.4.391

Fig 9.

Figure 9.

The outputs of FRS where phase A (close-in) is located at 3 km at 46 ms and C (remote-end) is located at 99 km at 46 ms, with the other output B located at 100 km, indicating that there exists an AC multi-location fault.

The International Journal of Fuzzy Logic and Intelligent Systems 2021; 21: 391-400https://doi.org/10.5391/IJFIS.2021.21.4.391

Table 1 . Effect of varying φ in multi-location fault scenario (A fault at 11 km and C fault at 78 km).

Φ (°)R (Ω)D1 (km)D2 (km)D3 (km)Error in phase
A faultC fault
102011.10110078.0830.1010.083
502010.88010078.1550.2200.155
1002011.23310077.9660.2330.034
1502011.14710077.8010.1470.199
2002011.02510078.0670.0250.067
2502010.73510078.2210.2650.221
3002011.25410078.0110.2540.011
3502011.21110078.2260.2110.226

Table 2 . Effect of varying R in multi-location fault scenario (A fault at 92 km and B fault at 21 km).

Φ (°)R (Ω)D1 (km)D2 (km)D3 (km)Error in phase
A faultC fault
901092.19220.8291000.1920.171
903092.03421.1861000.0340.186
905091.94321.1551000.0570.155
907092.09121.2451000.0910.245
909092.17621.0431000.1760.043
9011091.80321.0241000.1970.024
9013092.10621.0111000.1060.011
9015092.13121.1621000.1310.162

Table 3 . Effect of varying distances and types.

Φ (°)R (Ω)Fault-1Fault-2D1 (km)D2 (km)D3 (km)Error in Fault-1Error in Fault-2
4575A-Phase fault at 28 kmB-Phase fault at 83 km28.13483.2521000.1340.252
4575A-Phase fault at 32 kmC-Phase fault at 67 km31.98110067.2400.0290.240
4575B-Phase fault at 44 kmC-Phase fault at 15 km10043.86415.0660.1360.066
4575A-Phase fault at 65 kmB-Phase fault at 26 km64.95325.7621000.0470.248
4575A-Phase fault at 18 kmC-Phase fault at 94 km18.21310094.1250.2130.125
4575B-Phase fault at 19 kmC-Phase fault at 06 km10019.20106.1610.2010.161

Table 4 . Effect of varying shunt faults.

Φ (°)R (Ω)TypeDistance (km)D1 (km)D2 (km)D3 (km)Error
18050Phase-B fault91008.9261000.074
18050Phase-B fault1510015.0851000.085
18050Phase-B fault2810027.0681000.068
18050Phase-B fault3610035.8911000.009
18050Phase-B fault4410044.1041000.104
18050Phase-B fault5110051.2111000.211
18050Phase-B fault6010060.2821000.282
18050Phase-B fault6710066.1351000.135
18050Phase-B fault7310072.8231000.177
18050Phase-B fault8210082.2921000.292
18050Phase-B fault8910089.1241000.124
18050Phase-B fault9910098.7611000.239

Table 5 . Effect of close-in and remote-end multi-location faults.

Φ (°)R (Ω)Fault-1Fault-2D1 (km)D2 (km)D3 (km)Error in Fault-1Error in Fault-2
4575A-Phase fault at 1 kmB-Phase fault at 5 km1.16399.1891000.1630.189
4575A-Phase fault at 2 kmC-Phase fault at 4 km2.02010098.1420.0200.142
4575B-Phase fault at 3 kmC-Phase fault at 3 km1003.86397.0670.1370.067
4575A-Phase fault at 4 kmB-Phase fault at 2 km4.90096.7651000.1000.235
4575A-Phase fault at 5 kmC-Phase fault at 1 km5.24410095.0880.2440.088

Table 6 . Comparison of FRS to established techniques.

RefFault typesModel usedTest samplesError
[3]Multi-location faultsNeural networks-1
[10]Multi-location faultsNeural networks-1
[11]Cross-country faultsNeural networks-1
[12]Multi-location faultsFRS10000.5
[13]Cross-country faultsFRS20,0000.41
[14]Cross-country faultsSupport vector machines14,500-
[16]Simultaneous faultsFRS-0.4
Proposed FRSMulti-location faultsFRS250000.25

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