Swarm Intelligence-based Directional Relaying Approach for Power Network
Abstract In this article, swarm intelligence-based supportive threshold setting mechanism is introduced to enhance the directional relay performance during faults. For the estimation of fault direction, phase angle of positive sequence current (PPSC) is calculated using discrete Fourier transform. I...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Prasad, Ch. Durga [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Institution of Engineers (India) 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of the Institution of Engineers (India) - [New Delhi] : Springer India, 2012, 103(2021), 2 vom: 31. Aug., Seite 615-631 |
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| Übergeordnetes Werk: |
volume:103 ; year:2021 ; number:2 ; day:31 ; month:08 ; pages:615-631 |
| Links: |
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| DOI / URN: |
10.1007/s40031-021-00665-8 |
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| Katalog-ID: |
SPR046747605 |
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| 520 | |a Abstract In this article, swarm intelligence-based supportive threshold setting mechanism is introduced to enhance the directional relay performance during faults. For the estimation of fault direction, phase angle of positive sequence current (PPSC) is calculated using discrete Fourier transform. Instead of considering the immature decisions based on variation of PPSC directly, four swarm evaluated thresholds are extracted with particle swarm optimization algorithm (PSO) with an acceptable delay. Among four available thresholds, two thresholds are termed as main thresholds and other two are supportive thresholds. Together, three thresholds are involved in the process of detection and estimation of forward faults, and one threshold is used for reverse fault direction estimation. This combined threshold setting mechanism in directional relaying is a new attempt for its performance enhancement. The improvement of the proposed directional relay with supportive threshold setting mechanism is compared with the existing directional relay techniques and the simulation work is carried out in MATLAB-SIMULINK environment. The results carried out in this paper show the enhancement of the directional relay performance with additional intelligent thresholds which produce 100% accurate results against tested cases. | ||
| 650 | 4 | |a Directional relay |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Discrete Fourier transform (DFT) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Particle swarm optimization |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Biswal, Monalisa |4 aut | |
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10.1007/s40031-021-00665-8 doi (DE-627)SPR046747605 (SPR)s40031-021-00665-8-e DE-627 ger DE-627 rakwb eng Prasad, Ch. Durga verfasserin aut Swarm Intelligence-based Directional Relaying Approach for Power Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2021 Abstract In this article, swarm intelligence-based supportive threshold setting mechanism is introduced to enhance the directional relay performance during faults. For the estimation of fault direction, phase angle of positive sequence current (PPSC) is calculated using discrete Fourier transform. Instead of considering the immature decisions based on variation of PPSC directly, four swarm evaluated thresholds are extracted with particle swarm optimization algorithm (PSO) with an acceptable delay. Among four available thresholds, two thresholds are termed as main thresholds and other two are supportive thresholds. Together, three thresholds are involved in the process of detection and estimation of forward faults, and one threshold is used for reverse fault direction estimation. This combined threshold setting mechanism in directional relaying is a new attempt for its performance enhancement. The improvement of the proposed directional relay with supportive threshold setting mechanism is compared with the existing directional relay techniques and the simulation work is carried out in MATLAB-SIMULINK environment. The results carried out in this paper show the enhancement of the directional relay performance with additional intelligent thresholds which produce 100% accurate results against tested cases. Directional relay (dpeaa)DE-He213 Discrete Fourier transform (DFT) (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Biswal, Monalisa aut Enthalten in Journal of the Institution of Engineers (India) [New Delhi] : Springer India, 2012 103(2021), 2 vom: 31. Aug., Seite 615-631 (DE-627)722236980 (DE-600)2677588-8 2250-2114 nnns volume:103 year:2021 number:2 day:31 month:08 pages:615-631 https://dx.doi.org/10.1007/s40031-021-00665-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 103 2021 2 31 08 615-631 |
| spelling |
10.1007/s40031-021-00665-8 doi (DE-627)SPR046747605 (SPR)s40031-021-00665-8-e DE-627 ger DE-627 rakwb eng Prasad, Ch. Durga verfasserin aut Swarm Intelligence-based Directional Relaying Approach for Power Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2021 Abstract In this article, swarm intelligence-based supportive threshold setting mechanism is introduced to enhance the directional relay performance during faults. For the estimation of fault direction, phase angle of positive sequence current (PPSC) is calculated using discrete Fourier transform. Instead of considering the immature decisions based on variation of PPSC directly, four swarm evaluated thresholds are extracted with particle swarm optimization algorithm (PSO) with an acceptable delay. Among four available thresholds, two thresholds are termed as main thresholds and other two are supportive thresholds. Together, three thresholds are involved in the process of detection and estimation of forward faults, and one threshold is used for reverse fault direction estimation. This combined threshold setting mechanism in directional relaying is a new attempt for its performance enhancement. The improvement of the proposed directional relay with supportive threshold setting mechanism is compared with the existing directional relay techniques and the simulation work is carried out in MATLAB-SIMULINK environment. The results carried out in this paper show the enhancement of the directional relay performance with additional intelligent thresholds which produce 100% accurate results against tested cases. Directional relay (dpeaa)DE-He213 Discrete Fourier transform (DFT) (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Biswal, Monalisa aut Enthalten in Journal of the Institution of Engineers (India) [New Delhi] : Springer India, 2012 103(2021), 2 vom: 31. Aug., Seite 615-631 (DE-627)722236980 (DE-600)2677588-8 2250-2114 nnns volume:103 year:2021 number:2 day:31 month:08 pages:615-631 https://dx.doi.org/10.1007/s40031-021-00665-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 103 2021 2 31 08 615-631 |
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10.1007/s40031-021-00665-8 doi (DE-627)SPR046747605 (SPR)s40031-021-00665-8-e DE-627 ger DE-627 rakwb eng Prasad, Ch. Durga verfasserin aut Swarm Intelligence-based Directional Relaying Approach for Power Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2021 Abstract In this article, swarm intelligence-based supportive threshold setting mechanism is introduced to enhance the directional relay performance during faults. For the estimation of fault direction, phase angle of positive sequence current (PPSC) is calculated using discrete Fourier transform. Instead of considering the immature decisions based on variation of PPSC directly, four swarm evaluated thresholds are extracted with particle swarm optimization algorithm (PSO) with an acceptable delay. Among four available thresholds, two thresholds are termed as main thresholds and other two are supportive thresholds. Together, three thresholds are involved in the process of detection and estimation of forward faults, and one threshold is used for reverse fault direction estimation. This combined threshold setting mechanism in directional relaying is a new attempt for its performance enhancement. The improvement of the proposed directional relay with supportive threshold setting mechanism is compared with the existing directional relay techniques and the simulation work is carried out in MATLAB-SIMULINK environment. The results carried out in this paper show the enhancement of the directional relay performance with additional intelligent thresholds which produce 100% accurate results against tested cases. Directional relay (dpeaa)DE-He213 Discrete Fourier transform (DFT) (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Biswal, Monalisa aut Enthalten in Journal of the Institution of Engineers (India) [New Delhi] : Springer India, 2012 103(2021), 2 vom: 31. Aug., Seite 615-631 (DE-627)722236980 (DE-600)2677588-8 2250-2114 nnns volume:103 year:2021 number:2 day:31 month:08 pages:615-631 https://dx.doi.org/10.1007/s40031-021-00665-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 103 2021 2 31 08 615-631 |
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10.1007/s40031-021-00665-8 doi (DE-627)SPR046747605 (SPR)s40031-021-00665-8-e DE-627 ger DE-627 rakwb eng Prasad, Ch. Durga verfasserin aut Swarm Intelligence-based Directional Relaying Approach for Power Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2021 Abstract In this article, swarm intelligence-based supportive threshold setting mechanism is introduced to enhance the directional relay performance during faults. For the estimation of fault direction, phase angle of positive sequence current (PPSC) is calculated using discrete Fourier transform. Instead of considering the immature decisions based on variation of PPSC directly, four swarm evaluated thresholds are extracted with particle swarm optimization algorithm (PSO) with an acceptable delay. Among four available thresholds, two thresholds are termed as main thresholds and other two are supportive thresholds. Together, three thresholds are involved in the process of detection and estimation of forward faults, and one threshold is used for reverse fault direction estimation. This combined threshold setting mechanism in directional relaying is a new attempt for its performance enhancement. The improvement of the proposed directional relay with supportive threshold setting mechanism is compared with the existing directional relay techniques and the simulation work is carried out in MATLAB-SIMULINK environment. The results carried out in this paper show the enhancement of the directional relay performance with additional intelligent thresholds which produce 100% accurate results against tested cases. Directional relay (dpeaa)DE-He213 Discrete Fourier transform (DFT) (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Biswal, Monalisa aut Enthalten in Journal of the Institution of Engineers (India) [New Delhi] : Springer India, 2012 103(2021), 2 vom: 31. Aug., Seite 615-631 (DE-627)722236980 (DE-600)2677588-8 2250-2114 nnns volume:103 year:2021 number:2 day:31 month:08 pages:615-631 https://dx.doi.org/10.1007/s40031-021-00665-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 103 2021 2 31 08 615-631 |
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10.1007/s40031-021-00665-8 doi (DE-627)SPR046747605 (SPR)s40031-021-00665-8-e DE-627 ger DE-627 rakwb eng Prasad, Ch. Durga verfasserin aut Swarm Intelligence-based Directional Relaying Approach for Power Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Institution of Engineers (India) 2021 Abstract In this article, swarm intelligence-based supportive threshold setting mechanism is introduced to enhance the directional relay performance during faults. For the estimation of fault direction, phase angle of positive sequence current (PPSC) is calculated using discrete Fourier transform. Instead of considering the immature decisions based on variation of PPSC directly, four swarm evaluated thresholds are extracted with particle swarm optimization algorithm (PSO) with an acceptable delay. Among four available thresholds, two thresholds are termed as main thresholds and other two are supportive thresholds. Together, three thresholds are involved in the process of detection and estimation of forward faults, and one threshold is used for reverse fault direction estimation. This combined threshold setting mechanism in directional relaying is a new attempt for its performance enhancement. The improvement of the proposed directional relay with supportive threshold setting mechanism is compared with the existing directional relay techniques and the simulation work is carried out in MATLAB-SIMULINK environment. The results carried out in this paper show the enhancement of the directional relay performance with additional intelligent thresholds which produce 100% accurate results against tested cases. Directional relay (dpeaa)DE-He213 Discrete Fourier transform (DFT) (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 Biswal, Monalisa aut Enthalten in Journal of the Institution of Engineers (India) [New Delhi] : Springer India, 2012 103(2021), 2 vom: 31. Aug., Seite 615-631 (DE-627)722236980 (DE-600)2677588-8 2250-2114 nnns volume:103 year:2021 number:2 day:31 month:08 pages:615-631 https://dx.doi.org/10.1007/s40031-021-00665-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 103 2021 2 31 08 615-631 |
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Prasad, Ch. Durga |
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Prasad, Ch. Durga misc Directional relay misc Discrete Fourier transform (DFT) misc Particle swarm optimization Swarm Intelligence-based Directional Relaying Approach for Power Network |
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Swarm Intelligence-based Directional Relaying Approach for Power Network Directional relay (dpeaa)DE-He213 Discrete Fourier transform (DFT) (dpeaa)DE-He213 Particle swarm optimization (dpeaa)DE-He213 |
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swarm intelligence-based directional relaying approach for power network |
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Swarm Intelligence-based Directional Relaying Approach for Power Network |
| abstract |
Abstract In this article, swarm intelligence-based supportive threshold setting mechanism is introduced to enhance the directional relay performance during faults. For the estimation of fault direction, phase angle of positive sequence current (PPSC) is calculated using discrete Fourier transform. Instead of considering the immature decisions based on variation of PPSC directly, four swarm evaluated thresholds are extracted with particle swarm optimization algorithm (PSO) with an acceptable delay. Among four available thresholds, two thresholds are termed as main thresholds and other two are supportive thresholds. Together, three thresholds are involved in the process of detection and estimation of forward faults, and one threshold is used for reverse fault direction estimation. This combined threshold setting mechanism in directional relaying is a new attempt for its performance enhancement. The improvement of the proposed directional relay with supportive threshold setting mechanism is compared with the existing directional relay techniques and the simulation work is carried out in MATLAB-SIMULINK environment. The results carried out in this paper show the enhancement of the directional relay performance with additional intelligent thresholds which produce 100% accurate results against tested cases. © The Institution of Engineers (India) 2021 |
| abstractGer |
Abstract In this article, swarm intelligence-based supportive threshold setting mechanism is introduced to enhance the directional relay performance during faults. For the estimation of fault direction, phase angle of positive sequence current (PPSC) is calculated using discrete Fourier transform. Instead of considering the immature decisions based on variation of PPSC directly, four swarm evaluated thresholds are extracted with particle swarm optimization algorithm (PSO) with an acceptable delay. Among four available thresholds, two thresholds are termed as main thresholds and other two are supportive thresholds. Together, three thresholds are involved in the process of detection and estimation of forward faults, and one threshold is used for reverse fault direction estimation. This combined threshold setting mechanism in directional relaying is a new attempt for its performance enhancement. The improvement of the proposed directional relay with supportive threshold setting mechanism is compared with the existing directional relay techniques and the simulation work is carried out in MATLAB-SIMULINK environment. The results carried out in this paper show the enhancement of the directional relay performance with additional intelligent thresholds which produce 100% accurate results against tested cases. © The Institution of Engineers (India) 2021 |
| abstract_unstemmed |
Abstract In this article, swarm intelligence-based supportive threshold setting mechanism is introduced to enhance the directional relay performance during faults. For the estimation of fault direction, phase angle of positive sequence current (PPSC) is calculated using discrete Fourier transform. Instead of considering the immature decisions based on variation of PPSC directly, four swarm evaluated thresholds are extracted with particle swarm optimization algorithm (PSO) with an acceptable delay. Among four available thresholds, two thresholds are termed as main thresholds and other two are supportive thresholds. Together, three thresholds are involved in the process of detection and estimation of forward faults, and one threshold is used for reverse fault direction estimation. This combined threshold setting mechanism in directional relaying is a new attempt for its performance enhancement. The improvement of the proposed directional relay with supportive threshold setting mechanism is compared with the existing directional relay techniques and the simulation work is carried out in MATLAB-SIMULINK environment. The results carried out in this paper show the enhancement of the directional relay performance with additional intelligent thresholds which produce 100% accurate results against tested cases. © The Institution of Engineers (India) 2021 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR046747605</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230507153741.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220413s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s40031-021-00665-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR046747605</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s40031-021-00665-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Prasad, Ch. Durga</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Swarm Intelligence-based Directional Relaying Approach for Power Network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Institution of Engineers (India) 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this article, swarm intelligence-based supportive threshold setting mechanism is introduced to enhance the directional relay performance during faults. For the estimation of fault direction, phase angle of positive sequence current (PPSC) is calculated using discrete Fourier transform. Instead of considering the immature decisions based on variation of PPSC directly, four swarm evaluated thresholds are extracted with particle swarm optimization algorithm (PSO) with an acceptable delay. Among four available thresholds, two thresholds are termed as main thresholds and other two are supportive thresholds. Together, three thresholds are involved in the process of detection and estimation of forward faults, and one threshold is used for reverse fault direction estimation. This combined threshold setting mechanism in directional relaying is a new attempt for its performance enhancement. The improvement of the proposed directional relay with supportive threshold setting mechanism is compared with the existing directional relay techniques and the simulation work is carried out in MATLAB-SIMULINK environment. 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