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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)
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
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No potential conflicts of interest relevant to this article was reported.
Biographies
E-mail: ankamnaresh29@gmail.com
E-mail: ameshmalur037@gmail.com
E-mail: jagadhasaravanan@gmail.com
E-mail: bharathigururaj@gmail.com
E-mail: hodaerospace@sandipuniversity.edu.in
E-mail: chaitanya.k407@gmail.com
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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)
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.
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TCSCTL connection diagram.
Fig 2.
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Flow chart of proposed FRS framework.
Fig 3.
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Current coefficients during multi-location fault.
Fig 4.
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Fuzzy rule-based system.
Fig 5.
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The input, outputs, and their membership functions.
Fig 6.
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IF-THEN rules.
Fig 7.
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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.
Fig 8.
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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.
Fig 9.
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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.
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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 fault C fault 10 20 11.101 100 78.083 0.101 0.083 50 20 10.880 100 78.155 0.220 0.155 100 20 11.233 100 77.966 0.233 0.034 150 20 11.147 100 77.801 0.147 0.199 200 20 11.025 100 78.067 0.025 0.067 250 20 10.735 100 78.221 0.265 0.221 300 20 11.254 100 78.011 0.254 0.011 350 20 11.211 100 78.226 0.211 0.226
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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 fault C fault 90 10 92.192 20.829 100 0.192 0.171 90 30 92.034 21.186 100 0.034 0.186 90 50 91.943 21.155 100 0.057 0.155 90 70 92.091 21.245 100 0.091 0.245 90 90 92.176 21.043 100 0.176 0.043 90 110 91.803 21.024 100 0.197 0.024 90 130 92.106 21.011 100 0.106 0.011 90 150 92.131 21.162 100 0.131 0.162
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Table 3 . Effect of varying distances and types.
Φ (°) R (Ω) Fault-1 Fault-2 D1 (km) D2 (km) D3 (km) Error in Fault-1 Error in Fault-2 45 75 A-Phase fault at 28 km B-Phase fault at 83 km 28.134 83.252 100 0.134 0.252 45 75 A-Phase fault at 32 km C-Phase fault at 67 km 31.981 100 67.240 0.029 0.240 45 75 B-Phase fault at 44 km C-Phase fault at 15 km 100 43.864 15.066 0.136 0.066 45 75 A-Phase fault at 65 km B-Phase fault at 26 km 64.953 25.762 100 0.047 0.248 45 75 A-Phase fault at 18 km C-Phase fault at 94 km 18.213 100 94.125 0.213 0.125 45 75 B-Phase fault at 19 km C-Phase fault at 06 km 100 19.201 06.161 0.201 0.161
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Table 4 . Effect of varying shunt faults.
Φ (°) R (Ω) Type Distance (km) D1 (km) D2 (km) D3 (km) Error 180 50 Phase-B fault 9 100 8.926 100 0.074 180 50 Phase-B fault 15 100 15.085 100 0.085 180 50 Phase-B fault 28 100 27.068 100 0.068 180 50 Phase-B fault 36 100 35.891 100 0.009 180 50 Phase-B fault 44 100 44.104 100 0.104 180 50 Phase-B fault 51 100 51.211 100 0.211 180 50 Phase-B fault 60 100 60.282 100 0.282 180 50 Phase-B fault 67 100 66.135 100 0.135 180 50 Phase-B fault 73 100 72.823 100 0.177 180 50 Phase-B fault 82 100 82.292 100 0.292 180 50 Phase-B fault 89 100 89.124 100 0.124 180 50 Phase-B fault 99 100 98.761 100 0.239
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Table 5 . Effect of close-in and remote-end multi-location faults.
Φ (°) R (Ω) Fault-1 Fault-2 D1 (km) D2 (km) D3 (km) Error in Fault-1 Error in Fault-2 45 75 A-Phase fault at 1 km B-Phase fault at 5 km 1.163 99.189 100 0.163 0.189 45 75 A-Phase fault at 2 km C-Phase fault at 4 km 2.020 100 98.142 0.020 0.142 45 75 B-Phase fault at 3 km C-Phase fault at 3 km 100 3.863 97.067 0.137 0.067 45 75 A-Phase fault at 4 km B-Phase fault at 2 km 4.900 96.765 100 0.100 0.235 45 75 A-Phase fault at 5 km C-Phase fault at 1 km 5.244 100 95.088 0.244 0.088
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Table 6 . Comparison of FRS to established techniques.
Ref Fault types Model used Test samples Error [3] Multi-location faults Neural networks - 1 [10] Multi-location faults Neural networks - 1 [11] Cross-country faults Neural networks - 1 [12] Multi-location faults FRS 1000 0.5 [13] Cross-country faults FRS 20,000 0.41 [14] Cross-country faults Support vector machines 14,500 - [16] Simultaneous faults FRS - 0.4 Proposed FRS Multi-location faults FRS 25000 0.25
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