Optimal Power Flow using a Hybrid Particle Swarm Optimizer with Moth Flame Optimizer
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). Results show that hybrid PSO-MFO gives better optimization values as compared with PSO and MFO which verifies the effectiveness of the suggested algorithm.
