Find optimal capacity and location of distributed generation units in radial distribution networks by using enhanced coyote optimization algorithm

This paper proposes a novel effective optimization algorithm called enhanced coyote optimization algorithm (ECOA). This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization probl...

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Veröffentlicht in:Neural computing & applications 2021-05, Vol.33 (9), p.4343-4371
Hauptverfasser: Pham, Thai Dinh, Nguyen, Thang Trung, Dinh, Bach Hoang
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Dinh, Bach Hoang
description This paper proposes a novel effective optimization algorithm called enhanced coyote optimization algorithm (ECOA). This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce the power loss, operating costs as well as improve voltage stability. Based on the original coyote optimization algorithm (COA), ECOA is developed to be able to expand the search area and retain a good solution group in each generation. It includes two modifications to improve the efficiency of the original COA approach where the first one is replacing the central solution by the best current solution in the first new solution generation technique and the second focuses on reducing the computation burden and process time in the second new solution generation step. In this research, various experiments have been implemented by applying ECOA, COA as well as salp swarm algorithm (SSA), Sunflower optimization (SOA) for three IEEE radial distribution power networks with 33, 69 and 85 buses. Obtained results have been statistically analyzed to investigate the appropriate control parameters and to verify the performance of the proposed ECOA method. In addition, the performance of ECOA is also compared to various similar meta-heuristic methods such as genetic algorithm (GA), particle swarm optimization (PSO), hybrid genetic algorithm and particle swarm optimization (HGA-PSO), simulated annealing, bacterial foraging optimization algorithm, backtracking search optimization algorithm, harmony search algorithm, whale optimization algorithm (WOA) and combined power loss index-whale optimization algorithm (PLI-WOA). Detailed comparisons show that ECOA can determine more effective location and size of DGs with faster speed than other methods. Specifically, the improvement levels of the proposed method over compared to SFO, SSA, and COA can be up to 2.1978%, 0.7858% and 0.2348%. Furthermore, as compared to other existing methods in references, ECOA achieves the significant improvements which are up to 31.7491%, 20.2143% and 22.7213% for the three test systems, respectively. Thus, the proposed method is a favorable method in solving the optimal determination of DGs in radial distribution networks.
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This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce the power loss, operating costs as well as improve voltage stability. Based on the original coyote optimization algorithm (COA), ECOA is developed to be able to expand the search area and retain a good solution group in each generation. It includes two modifications to improve the efficiency of the original COA approach where the first one is replacing the central solution by the best current solution in the first new solution generation technique and the second focuses on reducing the computation burden and process time in the second new solution generation step. In this research, various experiments have been implemented by applying ECOA, COA as well as salp swarm algorithm (SSA), Sunflower optimization (SOA) for three IEEE radial distribution power networks with 33, 69 and 85 buses. Obtained results have been statistically analyzed to investigate the appropriate control parameters and to verify the performance of the proposed ECOA method. In addition, the performance of ECOA is also compared to various similar meta-heuristic methods such as genetic algorithm (GA), particle swarm optimization (PSO), hybrid genetic algorithm and particle swarm optimization (HGA-PSO), simulated annealing, bacterial foraging optimization algorithm, backtracking search optimization algorithm, harmony search algorithm, whale optimization algorithm (WOA) and combined power loss index-whale optimization algorithm (PLI-WOA). Detailed comparisons show that ECOA can determine more effective location and size of DGs with faster speed than other methods. Specifically, the improvement levels of the proposed method over compared to SFO, SSA, and COA can be up to 2.1978%, 0.7858% and 0.2348%. Furthermore, as compared to other existing methods in references, ECOA achieves the significant improvements which are up to 31.7491%, 20.2143% and 22.7213% for the three test systems, respectively. 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This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce the power loss, operating costs as well as improve voltage stability. Based on the original coyote optimization algorithm (COA), ECOA is developed to be able to expand the search area and retain a good solution group in each generation. It includes two modifications to improve the efficiency of the original COA approach where the first one is replacing the central solution by the best current solution in the first new solution generation technique and the second focuses on reducing the computation burden and process time in the second new solution generation step. In this research, various experiments have been implemented by applying ECOA, COA as well as salp swarm algorithm (SSA), Sunflower optimization (SOA) for three IEEE radial distribution power networks with 33, 69 and 85 buses. Obtained results have been statistically analyzed to investigate the appropriate control parameters and to verify the performance of the proposed ECOA method. In addition, the performance of ECOA is also compared to various similar meta-heuristic methods such as genetic algorithm (GA), particle swarm optimization (PSO), hybrid genetic algorithm and particle swarm optimization (HGA-PSO), simulated annealing, bacterial foraging optimization algorithm, backtracking search optimization algorithm, harmony search algorithm, whale optimization algorithm (WOA) and combined power loss index-whale optimization algorithm (PLI-WOA). Detailed comparisons show that ECOA can determine more effective location and size of DGs with faster speed than other methods. Specifically, the improvement levels of the proposed method over compared to SFO, SSA, and COA can be up to 2.1978%, 0.7858% and 0.2348%. Furthermore, as compared to other existing methods in references, ECOA achieves the significant improvements which are up to 31.7491%, 20.2143% and 22.7213% for the three test systems, respectively. 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Nguyen, Thang Trung ; Dinh, Bach Hoang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-c0e25ecfbd07f5bb904f00cde69e1ed5cdf64a5d453ac915c14f6bf7d22edaf43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial Intelligence</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Distributed generation</topic><topic>Electric power distribution</topic><topic>Genetic algorithms</topic><topic>Heuristic methods</topic><topic>Image Processing and Computer Vision</topic><topic>Multiple objective analysis</topic><topic>Networks</topic><topic>Optimization algorithms</topic><topic>Original Article</topic><topic>Particle swarm optimization</topic><topic>Probability and Statistics in Computer Science</topic><topic>Radial distribution</topic><topic>Search algorithms</topic><topic>Simulated annealing</topic><topic>Sunflowers</topic><topic>Voltage stability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pham, Thai Dinh</creatorcontrib><creatorcontrib>Nguyen, Thang Trung</creatorcontrib><creatorcontrib>Dinh, Bach Hoang</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; 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applications</jtitle><stitle>Neural Comput &amp; Applic</stitle><date>2021-05-01</date><risdate>2021</risdate><volume>33</volume><issue>9</issue><spage>4343</spage><epage>4371</epage><pages>4343-4371</pages><issn>0941-0643</issn><eissn>1433-3058</eissn><abstract>This paper proposes a novel effective optimization algorithm called enhanced coyote optimization algorithm (ECOA). This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce the power loss, operating costs as well as improve voltage stability. Based on the original coyote optimization algorithm (COA), ECOA is developed to be able to expand the search area and retain a good solution group in each generation. It includes two modifications to improve the efficiency of the original COA approach where the first one is replacing the central solution by the best current solution in the first new solution generation technique and the second focuses on reducing the computation burden and process time in the second new solution generation step. In this research, various experiments have been implemented by applying ECOA, COA as well as salp swarm algorithm (SSA), Sunflower optimization (SOA) for three IEEE radial distribution power networks with 33, 69 and 85 buses. Obtained results have been statistically analyzed to investigate the appropriate control parameters and to verify the performance of the proposed ECOA method. In addition, the performance of ECOA is also compared to various similar meta-heuristic methods such as genetic algorithm (GA), particle swarm optimization (PSO), hybrid genetic algorithm and particle swarm optimization (HGA-PSO), simulated annealing, bacterial foraging optimization algorithm, backtracking search optimization algorithm, harmony search algorithm, whale optimization algorithm (WOA) and combined power loss index-whale optimization algorithm (PLI-WOA). Detailed comparisons show that ECOA can determine more effective location and size of DGs with faster speed than other methods. Specifically, the improvement levels of the proposed method over compared to SFO, SSA, and COA can be up to 2.1978%, 0.7858% and 0.2348%. Furthermore, as compared to other existing methods in references, ECOA achieves the significant improvements which are up to 31.7491%, 20.2143% and 22.7213% for the three test systems, respectively. Thus, the proposed method is a favorable method in solving the optimal determination of DGs in radial distribution networks.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-020-05239-1</doi><tpages>29</tpages><orcidid>https://orcid.org/0000-0002-0951-410X</orcidid></addata></record>
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subjects Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Distributed generation
Electric power distribution
Genetic algorithms
Heuristic methods
Image Processing and Computer Vision
Multiple objective analysis
Networks
Optimization algorithms
Original Article
Particle swarm optimization
Probability and Statistics in Computer Science
Radial distribution
Search algorithms
Simulated annealing
Sunflowers
Voltage stability
title Find optimal capacity and location of distributed generation units in radial distribution networks by using enhanced coyote optimization algorithm
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