An efficient optimizer for optimal overcurrent relay coordination in power distribution system
In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcu...
Ausführliche Beschreibung
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| Autor*in: |
Acharya, Debasis [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Übergeordnetes Werk: |
Enthalten in: Do denture processing techniques affect the mechanical properties of denture teeth? - Clements, Jody L. ELSEVIER, 2017, an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:199 ; year:2022 ; day:1 ; month:08 ; pages:0 |
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| DOI / URN: |
10.1016/j.eswa.2022.116858 |
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ELV057508194 |
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| 520 | |a In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. | ||
| 520 | |a In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. | ||
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10.1016/j.eswa.2022.116858 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001764.pica (DE-627)ELV057508194 (ELSEVIER)S0957-4174(22)00308-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Acharya, Debasis verfasserin aut An efficient optimizer for optimal overcurrent relay coordination in power distribution system 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. Optimization algorithm Elsevier Time dial setting (TDS) Elsevier Coordination time interval (CTI) Elsevier Plug setting (PS) Elsevier Das, Dushmanta Kumar oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:199 year:2022 day:1 month:08 pages:0 https://doi.org/10.1016/j.eswa.2022.116858 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 199 2022 1 0801 0 |
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10.1016/j.eswa.2022.116858 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001764.pica (DE-627)ELV057508194 (ELSEVIER)S0957-4174(22)00308-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Acharya, Debasis verfasserin aut An efficient optimizer for optimal overcurrent relay coordination in power distribution system 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. Optimization algorithm Elsevier Time dial setting (TDS) Elsevier Coordination time interval (CTI) Elsevier Plug setting (PS) Elsevier Das, Dushmanta Kumar oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:199 year:2022 day:1 month:08 pages:0 https://doi.org/10.1016/j.eswa.2022.116858 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 199 2022 1 0801 0 |
| allfields_unstemmed |
10.1016/j.eswa.2022.116858 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001764.pica (DE-627)ELV057508194 (ELSEVIER)S0957-4174(22)00308-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Acharya, Debasis verfasserin aut An efficient optimizer for optimal overcurrent relay coordination in power distribution system 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. Optimization algorithm Elsevier Time dial setting (TDS) Elsevier Coordination time interval (CTI) Elsevier Plug setting (PS) Elsevier Das, Dushmanta Kumar oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:199 year:2022 day:1 month:08 pages:0 https://doi.org/10.1016/j.eswa.2022.116858 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 199 2022 1 0801 0 |
| allfieldsGer |
10.1016/j.eswa.2022.116858 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001764.pica (DE-627)ELV057508194 (ELSEVIER)S0957-4174(22)00308-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Acharya, Debasis verfasserin aut An efficient optimizer for optimal overcurrent relay coordination in power distribution system 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. Optimization algorithm Elsevier Time dial setting (TDS) Elsevier Coordination time interval (CTI) Elsevier Plug setting (PS) Elsevier Das, Dushmanta Kumar oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:199 year:2022 day:1 month:08 pages:0 https://doi.org/10.1016/j.eswa.2022.116858 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 199 2022 1 0801 0 |
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10.1016/j.eswa.2022.116858 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001764.pica (DE-627)ELV057508194 (ELSEVIER)S0957-4174(22)00308-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.96 bkl Acharya, Debasis verfasserin aut An efficient optimizer for optimal overcurrent relay coordination in power distribution system 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. Optimization algorithm Elsevier Time dial setting (TDS) Elsevier Coordination time interval (CTI) Elsevier Plug setting (PS) Elsevier Das, Dushmanta Kumar oth Enthalten in Elsevier Science Clements, Jody L. ELSEVIER Do denture processing techniques affect the mechanical properties of denture teeth? 2017 an international journal Amsterdam [u.a.] (DE-627)ELV000222070 volume:199 year:2022 day:1 month:08 pages:0 https://doi.org/10.1016/j.eswa.2022.116858 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.96 Zahnmedizin VZ AR 199 2022 1 0801 0 |
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an efficient optimizer for optimal overcurrent relay coordination in power distribution system |
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An efficient optimizer for optimal overcurrent relay coordination in power distribution system |
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In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. |
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In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. |
| abstract_unstemmed |
In this paper, a student-learning strategy based efficient optimizer has been developed to solve optimal over current relay coordination problem in the power system. The power system is a massive, interrelated network with inherent nonlinearity. The conventional analytical methods to solve an overcurrent relay coordination issue is difficult for such systems. Therefore, the metaheuristic optimization technique can be a good choice for relay coordination problem in the power network. Class topper optimization (CTO) is a stochastic technique inspired by the student learning behavior to improve his/her performance to be the best in his/her class which is successfully applied for different optimization problems. Like the other metaheuristic algorithm, CTO faces the problem of local optima with premature convergence to deal with complex problems. In this paper, a sigmoid acceleration coefficient based chaotic search class topper optimization (SCCTO) is proposed to develop an efficient optimizer and applied to solve optimal relay coordination problem. The logistic map based chaotic search is used at initialization stage of SCCTO algorithm. In the update stage of the proposed algorithm, a slowly varying sigmoid function is used along with normal varying function. It will keep diversity in local solution to explore the optimum global solution in the search space. To validate the effectiveness of the proposed algorithm to solve an optimization problem, the optimal overcurrent relay coordination in the distribution network is tested. The optimal setting of time dial of the relay (over current) with total operating time minimization is the aim of such optimization problem considering relay coordination time gap as constraints. Three types of power distribution networks are examined optimal relay coordination problems. The simulation result shows the efficiency of the developed optimizer in relay coordination with an improvement of 26 to 35 % in total relay operating time than existing results. |
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