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In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel hybrid meta-heuristic optimization algorithm Particle Swarm Optimization-Moth Flame Optimizer (HPSO-MFO) method. Hybrid PSO-MFO is a combination of PSO used for exploitation phase and MFO for exploration phase in an uncertain environment. Position and Speed of particle are reorganized according to Moth and flame location in each iteration. The hybrid PSO-MFO method has a fast convergence rate due to the use of roulette wheel selection method. For the OPF solution, standard IEEE-30 bus test system is used. The hybrid PSO-MFO method is implemented to solve the proposed problem. The problems considered in the OPF are fuel cost reduction, Voltage profile improvement, Voltage stability enhancement, Active power loss minimization and Reactive power loss minimization. The results obtained with hybrid PSO-MFO method is compared with other techniques such as Particle Swarm Optimization (PSO) and Moth Flame Optimizer (MFO).
Pradeep Jangir. 2017. \u201cOptimal Power Flow using a Hybrid Particle Swarm Optimizer with Moth Flame Optimizer\u201d. Global Journal of Research in Engineering - F: Electrical & Electronic GJRE-F Volume 17 (GJRE Volume 17 Issue F5): .
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
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Total Score: 102
Country: India
Subject: Global Journal of Research in Engineering - F: Electrical & Electronic
Authors: Pradeep Jangir, Narottam Jangir (PhD/Dr. count: 0)
View Count (all-time): 197
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Publish Date: 2017 10, Mon
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In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel hybrid meta-heuristic optimization algorithm Particle Swarm Optimization-Moth Flame Optimizer (HPSO-MFO) method. Hybrid PSO-MFO is a combination of PSO used for exploitation phase and MFO for exploration phase in an uncertain environment. Position and Speed of particle are reorganized according to Moth and flame location in each iteration. The hybrid PSO-MFO method has a fast convergence rate due to the use of roulette wheel selection method. For the OPF solution, standard IEEE-30 bus test system is used. The hybrid PSO-MFO method is implemented to solve the proposed problem. The problems considered in the OPF are fuel cost reduction, Voltage profile improvement, Voltage stability enhancement, Active power loss minimization and Reactive power loss minimization. The results obtained with hybrid PSO-MFO method is compared with other techniques such as Particle Swarm Optimization (PSO) and Moth Flame Optimizer (MFO).
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