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Kanagasabai Lenin
University Kukatpally
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm for Solving Optimal Reactive Power Dispatch Problem Lenin, Kanagasabai
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 1 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (467.852 KB) | DOI: 10.35877/454RI.asci31106

Abstract

In this work Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm (HGPSOS) has been done for solving the power dispatch problem. Genetic particle swarm optimization problem has been hybridized with Symbiotic organisms search (SOS) algorithm to solve the problem. Genetic particle swarm optimization algorithm is formed by combining the Particle swarm optimization algorithm (PSO) with genetic algorithm (GA). Symbiotic organisms search algorithm is based on the actions between two different organisms in the ecosystem- mutualism, commensalism and parasitism. Exploration process has been instigated capriciously and every organism specifies a solution with fitness value. Projected HGPSOS algorithm improves the quality of the search. Proposed HGPSOS algorithm is tested in IEEE 30, bus test system- power loss minimization, voltage deviation minimization and voltage stability enhancement has been attained.
Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm for Solving Optimal Reactive Power Dispatch Problem Lenin, Kanagasabai
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 1 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (467.852 KB) | DOI: 10.35877/454RI.asci31106

Abstract

In this work Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm (HGPSOS) has been done for solving the power dispatch problem. Genetic particle swarm optimization problem has been hybridized with Symbiotic organisms search (SOS) algorithm to solve the problem. Genetic particle swarm optimization algorithm is formed by combining the Particle swarm optimization algorithm (PSO) with genetic algorithm (GA). Symbiotic organisms search algorithm is based on the actions between two different organisms in the ecosystem- mutualism, commensalism and parasitism. Exploration process has been instigated capriciously and every organism specifies a solution with fitness value. Projected HGPSOS algorithm improves the quality of the search. Proposed HGPSOS algorithm is tested in IEEE 30, bus test system- power loss minimization, voltage deviation minimization and voltage stability enhancement has been attained.
Factual Power Loss Diminution by Enhanced Frog Leaping Algorithm Lenin, Kanagasabai
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (435.296 KB) | DOI: 10.35877/454RI.asci112

Abstract

This paper proposes Enhanced Frog Leaping Algorithm (EFLA) to solve the optimal reactive power problem. Frog leaping algorithm (FLA) replicates the procedure of frogs passing though the wetland and foraging deeds. Set of virtual frogs alienated into numerous groups known as “memeplexes”. Frog’s position’s turn out to be closer in every memeplex after few optimization runs and certainly, this crisis direct to premature convergence. In the proposed Enhanced Frog Leaping Algorithm (EFLA) the most excellent frog information is used to augment the local search in each memeplex and initiate to the exploration bound acceleration. To advance the speed of convergence two acceleration factors are introduced in the exploration plan formulation. Proposed Enhanced Frog Leaping Algorithm (EFLA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.
Factual Power Loss Diminution by Enhanced Frog Leaping Algorithm Lenin, Kanagasabai
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (435.296 KB) | DOI: 10.35877/454RI.asci112

Abstract

This paper proposes Enhanced Frog Leaping Algorithm (EFLA) to solve the optimal reactive power problem. Frog leaping algorithm (FLA) replicates the procedure of frogs passing though the wetland and foraging deeds. Set of virtual frogs alienated into numerous groups known as “memeplexes”. Frog’s position’s turn out to be closer in every memeplex after few optimization runs and certainly, this crisis direct to premature convergence. In the proposed Enhanced Frog Leaping Algorithm (EFLA) the most excellent frog information is used to augment the local search in each memeplex and initiate to the exploration bound acceleration. To advance the speed of convergence two acceleration factors are introduced in the exploration plan formulation. Proposed Enhanced Frog Leaping Algorithm (EFLA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.
Solving Optimal Reactive Power Dispatch Problem by Chaotic Based Brain Storm Optimization Algorithm Lenin, Kanagasabai
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.007 KB) | DOI: 10.35877/454RI.asci113

Abstract

In this work Chaotic Predator-Prey Brain Storm Optimization (CPS) algorithm is proposed to solve optimal reactive power dispatch problem. Predator–Prey Brain Storm Optimization position cluster centers to execute as predators, accordingly it will progress towards enhanced positions, although the left over thoughts do as preys; consequently they move far from their neighboring predators. In the projected algorithm chaotic theory has been applied to enhance the quality of the exploration. Ergodicity and indiscretion are utilized in the CPS algorithm, such that projected algorithm will not get trapped in the local optimal solution. Chaotic predator-prey brain storm optimization (CPS) algorithm has been tested in standard IEEE 30 bus test system and results show the projected algorithm reduced the real power loss effectively.
Solving Optimal Reactive Power Dispatch Problem by Chaotic Based Brain Storm Optimization Algorithm Lenin, Kanagasabai
Journal of Applied Science, Engineering, Technology, and Education Vol. 3 No. 2 (2021)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.007 KB) | DOI: 10.35877/454RI.asci113

Abstract

In this work Chaotic Predator-Prey Brain Storm Optimization (CPS) algorithm is proposed to solve optimal reactive power dispatch problem. Predator–Prey Brain Storm Optimization position cluster centers to execute as predators, accordingly it will progress towards enhanced positions, although the left over thoughts do as preys; consequently they move far from their neighboring predators. In the projected algorithm chaotic theory has been applied to enhance the quality of the exploration. Ergodicity and indiscretion are utilized in the CPS algorithm, such that projected algorithm will not get trapped in the local optimal solution. Chaotic predator-prey brain storm optimization (CPS) algorithm has been tested in standard IEEE 30 bus test system and results show the projected algorithm reduced the real power loss effectively.
Real Power Loss Reduction by Amplified Water Cycle Algorithm Lenin, Kanagasabai
Journal of Applied Science, Engineering, Technology, and Education Vol. 2 No. 1 (2020)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (569.287 KB) | DOI: 10.35877/454RI.asci2166

Abstract

In this paper Amplified Water Cycle Algorithm (AWCA) has been used to solve the optimal reactive power problem. Water cycle algorithm (WCA) is a methodology which inspired by the hydrological cycle which happen in nature. In this work water cycle algorithm hybridized with Gravitational Search Algorithm, Chaos theory. In the projected Amplified Water Cycle Algorithm (AWCA) - with reference to the fitness value, population is first alienated into three groups: streams, rivers and sea. Through this hybridization exploration and exploitation is effectively improved. Positions of particles are initially modernized according to gravitational search. Chaos theory is then defined and integrated in water cycle algorithm to modernize the population which will augment explore capability and population diversity. Projected Amplified Water Cycle Algorithm (AWCA) has been tested in standard IEEE 14, 30, 57, 300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.
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