Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm
Abstract Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution b...
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| Autor*in: |
Kalage, Amol A. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media Singapore 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Intelligent industrial systems - Berlin : Springer, 2015, 2(2016), 1 vom: März, Seite 55-71 |
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| Übergeordnetes Werk: |
volume:2 ; year:2016 ; number:1 ; month:03 ; pages:55-71 |
| Links: |
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| DOI / URN: |
10.1007/s40903-016-0038-9 |
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| Katalog-ID: |
SPR037987143 |
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| 520 | |a Abstract Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution becomes difficult to find with shorter range of design variables. This paper presents modified adaptive teaching learning based optimization algorithm to overcome this drawback of conventional heuristic techniques. The coordination problem is formulated as a constrained non-linear optimization problem to determine the optimum solution for the time multiplier setting (TMS) and plug setting (PS) of DOCRs. Initial solution for TMS is heuristically obtained with the commonly chosen widest range for TMS values. The upper bound of TMS range then substituted by the maximum TMS value in the first initial solution. The new upper limit is obviously lower than the earlier one. Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies have been presented here to show the effectiveness of the proposed algorithm. The impact of distributed generation (DG) and application of superconducting fault current limiter to mitigate DG impact is presented in case study—III. | ||
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10.1007/s40903-016-0038-9 doi (DE-627)SPR037987143 (SPR)s40903-016-0038-9-e DE-627 ger DE-627 rakwb eng Kalage, Amol A. verfasserin aut Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media Singapore 2016 Abstract Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution becomes difficult to find with shorter range of design variables. This paper presents modified adaptive teaching learning based optimization algorithm to overcome this drawback of conventional heuristic techniques. The coordination problem is formulated as a constrained non-linear optimization problem to determine the optimum solution for the time multiplier setting (TMS) and plug setting (PS) of DOCRs. Initial solution for TMS is heuristically obtained with the commonly chosen widest range for TMS values. The upper bound of TMS range then substituted by the maximum TMS value in the first initial solution. The new upper limit is obviously lower than the earlier one. Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies have been presented here to show the effectiveness of the proposed algorithm. The impact of distributed generation (DG) and application of superconducting fault current limiter to mitigate DG impact is presented in case study—III. Relay coordination (dpeaa)DE-He213 TLBO (dpeaa)DE-He213 Non-linear programming (dpeaa)DE-He213 Overcurrent relays (dpeaa)DE-He213 Backup protection (dpeaa)DE-He213 Ghawghawe, Nitin D. aut Enthalten in Intelligent industrial systems Berlin : Springer, 2015 2(2016), 1 vom: März, Seite 55-71 (DE-627)827026765 (DE-600)2822999-X 2199-854X nnns volume:2 year:2016 number:1 month:03 pages:55-71 https://dx.doi.org/10.1007/s40903-016-0038-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_285 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_4313 GBV_ILN_4328 GBV_ILN_4333 AR 2 2016 1 03 55-71 |
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10.1007/s40903-016-0038-9 doi (DE-627)SPR037987143 (SPR)s40903-016-0038-9-e DE-627 ger DE-627 rakwb eng Kalage, Amol A. verfasserin aut Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media Singapore 2016 Abstract Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution becomes difficult to find with shorter range of design variables. This paper presents modified adaptive teaching learning based optimization algorithm to overcome this drawback of conventional heuristic techniques. The coordination problem is formulated as a constrained non-linear optimization problem to determine the optimum solution for the time multiplier setting (TMS) and plug setting (PS) of DOCRs. Initial solution for TMS is heuristically obtained with the commonly chosen widest range for TMS values. The upper bound of TMS range then substituted by the maximum TMS value in the first initial solution. The new upper limit is obviously lower than the earlier one. Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies have been presented here to show the effectiveness of the proposed algorithm. The impact of distributed generation (DG) and application of superconducting fault current limiter to mitigate DG impact is presented in case study—III. Relay coordination (dpeaa)DE-He213 TLBO (dpeaa)DE-He213 Non-linear programming (dpeaa)DE-He213 Overcurrent relays (dpeaa)DE-He213 Backup protection (dpeaa)DE-He213 Ghawghawe, Nitin D. aut Enthalten in Intelligent industrial systems Berlin : Springer, 2015 2(2016), 1 vom: März, Seite 55-71 (DE-627)827026765 (DE-600)2822999-X 2199-854X nnns volume:2 year:2016 number:1 month:03 pages:55-71 https://dx.doi.org/10.1007/s40903-016-0038-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_285 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_4313 GBV_ILN_4328 GBV_ILN_4333 AR 2 2016 1 03 55-71 |
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10.1007/s40903-016-0038-9 doi (DE-627)SPR037987143 (SPR)s40903-016-0038-9-e DE-627 ger DE-627 rakwb eng Kalage, Amol A. verfasserin aut Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media Singapore 2016 Abstract Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution becomes difficult to find with shorter range of design variables. This paper presents modified adaptive teaching learning based optimization algorithm to overcome this drawback of conventional heuristic techniques. The coordination problem is formulated as a constrained non-linear optimization problem to determine the optimum solution for the time multiplier setting (TMS) and plug setting (PS) of DOCRs. Initial solution for TMS is heuristically obtained with the commonly chosen widest range for TMS values. The upper bound of TMS range then substituted by the maximum TMS value in the first initial solution. The new upper limit is obviously lower than the earlier one. Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies have been presented here to show the effectiveness of the proposed algorithm. The impact of distributed generation (DG) and application of superconducting fault current limiter to mitigate DG impact is presented in case study—III. Relay coordination (dpeaa)DE-He213 TLBO (dpeaa)DE-He213 Non-linear programming (dpeaa)DE-He213 Overcurrent relays (dpeaa)DE-He213 Backup protection (dpeaa)DE-He213 Ghawghawe, Nitin D. aut Enthalten in Intelligent industrial systems Berlin : Springer, 2015 2(2016), 1 vom: März, Seite 55-71 (DE-627)827026765 (DE-600)2822999-X 2199-854X nnns volume:2 year:2016 number:1 month:03 pages:55-71 https://dx.doi.org/10.1007/s40903-016-0038-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_285 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_4313 GBV_ILN_4328 GBV_ILN_4333 AR 2 2016 1 03 55-71 |
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10.1007/s40903-016-0038-9 doi (DE-627)SPR037987143 (SPR)s40903-016-0038-9-e DE-627 ger DE-627 rakwb eng Kalage, Amol A. verfasserin aut Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media Singapore 2016 Abstract Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution becomes difficult to find with shorter range of design variables. This paper presents modified adaptive teaching learning based optimization algorithm to overcome this drawback of conventional heuristic techniques. The coordination problem is formulated as a constrained non-linear optimization problem to determine the optimum solution for the time multiplier setting (TMS) and plug setting (PS) of DOCRs. Initial solution for TMS is heuristically obtained with the commonly chosen widest range for TMS values. The upper bound of TMS range then substituted by the maximum TMS value in the first initial solution. The new upper limit is obviously lower than the earlier one. Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies have been presented here to show the effectiveness of the proposed algorithm. The impact of distributed generation (DG) and application of superconducting fault current limiter to mitigate DG impact is presented in case study—III. Relay coordination (dpeaa)DE-He213 TLBO (dpeaa)DE-He213 Non-linear programming (dpeaa)DE-He213 Overcurrent relays (dpeaa)DE-He213 Backup protection (dpeaa)DE-He213 Ghawghawe, Nitin D. aut Enthalten in Intelligent industrial systems Berlin : Springer, 2015 2(2016), 1 vom: März, Seite 55-71 (DE-627)827026765 (DE-600)2822999-X 2199-854X nnns volume:2 year:2016 number:1 month:03 pages:55-71 https://dx.doi.org/10.1007/s40903-016-0038-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_285 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_4313 GBV_ILN_4328 GBV_ILN_4333 AR 2 2016 1 03 55-71 |
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10.1007/s40903-016-0038-9 doi (DE-627)SPR037987143 (SPR)s40903-016-0038-9-e DE-627 ger DE-627 rakwb eng Kalage, Amol A. verfasserin aut Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media Singapore 2016 Abstract Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution becomes difficult to find with shorter range of design variables. This paper presents modified adaptive teaching learning based optimization algorithm to overcome this drawback of conventional heuristic techniques. The coordination problem is formulated as a constrained non-linear optimization problem to determine the optimum solution for the time multiplier setting (TMS) and plug setting (PS) of DOCRs. Initial solution for TMS is heuristically obtained with the commonly chosen widest range for TMS values. The upper bound of TMS range then substituted by the maximum TMS value in the first initial solution. The new upper limit is obviously lower than the earlier one. Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies have been presented here to show the effectiveness of the proposed algorithm. The impact of distributed generation (DG) and application of superconducting fault current limiter to mitigate DG impact is presented in case study—III. Relay coordination (dpeaa)DE-He213 TLBO (dpeaa)DE-He213 Non-linear programming (dpeaa)DE-He213 Overcurrent relays (dpeaa)DE-He213 Backup protection (dpeaa)DE-He213 Ghawghawe, Nitin D. aut Enthalten in Intelligent industrial systems Berlin : Springer, 2015 2(2016), 1 vom: März, Seite 55-71 (DE-627)827026765 (DE-600)2822999-X 2199-854X nnns volume:2 year:2016 number:1 month:03 pages:55-71 https://dx.doi.org/10.1007/s40903-016-0038-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_285 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_4313 GBV_ILN_4328 GBV_ILN_4333 AR 2 2016 1 03 55-71 |
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Kalage, Amol A. |
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Kalage, Amol A. misc Relay coordination misc TLBO misc Non-linear programming misc Overcurrent relays misc Backup protection Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm |
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Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm Relay coordination (dpeaa)DE-He213 TLBO (dpeaa)DE-He213 Non-linear programming (dpeaa)DE-He213 Overcurrent relays (dpeaa)DE-He213 Backup protection (dpeaa)DE-He213 |
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Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm |
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Abstract Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution becomes difficult to find with shorter range of design variables. This paper presents modified adaptive teaching learning based optimization algorithm to overcome this drawback of conventional heuristic techniques. The coordination problem is formulated as a constrained non-linear optimization problem to determine the optimum solution for the time multiplier setting (TMS) and plug setting (PS) of DOCRs. Initial solution for TMS is heuristically obtained with the commonly chosen widest range for TMS values. The upper bound of TMS range then substituted by the maximum TMS value in the first initial solution. The new upper limit is obviously lower than the earlier one. Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies have been presented here to show the effectiveness of the proposed algorithm. The impact of distributed generation (DG) and application of superconducting fault current limiter to mitigate DG impact is presented in case study—III. © Springer Science+Business Media Singapore 2016 |
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Abstract Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution becomes difficult to find with shorter range of design variables. This paper presents modified adaptive teaching learning based optimization algorithm to overcome this drawback of conventional heuristic techniques. The coordination problem is formulated as a constrained non-linear optimization problem to determine the optimum solution for the time multiplier setting (TMS) and plug setting (PS) of DOCRs. Initial solution for TMS is heuristically obtained with the commonly chosen widest range for TMS values. The upper bound of TMS range then substituted by the maximum TMS value in the first initial solution. The new upper limit is obviously lower than the earlier one. Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies have been presented here to show the effectiveness of the proposed algorithm. The impact of distributed generation (DG) and application of superconducting fault current limiter to mitigate DG impact is presented in case study—III. © Springer Science+Business Media Singapore 2016 |
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Abstract Relay coordination problem is highly constrained optimization problem. Heuristic techniques are often used to solve optimization problem. These techniques have a drawback of converging to a non-optimum solution due to the wide range of design variables. On the other hand, initial solution becomes difficult to find with shorter range of design variables. This paper presents modified adaptive teaching learning based optimization algorithm to overcome this drawback of conventional heuristic techniques. The coordination problem is formulated as a constrained non-linear optimization problem to determine the optimum solution for the time multiplier setting (TMS) and plug setting (PS) of DOCRs. Initial solution for TMS is heuristically obtained with the commonly chosen widest range for TMS values. The upper bound of TMS range then substituted by the maximum TMS value in the first initial solution. The new upper limit is obviously lower than the earlier one. Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies have been presented here to show the effectiveness of the proposed algorithm. The impact of distributed generation (DG) and application of superconducting fault current limiter to mitigate DG impact is presented in case study—III. © Springer Science+Business Media Singapore 2016 |
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Next phase of optimization is carried out with the new range of TMS for the pre-determined iterations of teacher phase. Consequent to the completion of the teacher phase, new upper bound is obtained from the available solution and optimization is carried out for the pre-determined iterations of learner phase. This process is repeated to get the optimum solution. Fixed range for PS is used to obtain the selectivity. Such a strategy of iteratively updating the upper bound of TMS range shows remarkable improvement over the techniques which employ fixed TMS range. This algorithm is tested on different networks and has been found more effective. Four case studies have been presented here to show the effectiveness of the proposed algorithm. 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