ACTUAL POWER LOSS REDUCTION BY AUGMENTED PARTICLE SWARM OPTIMIZATION ALGORITHM

K Lenin

doi:10.29121/granthaalayah.v6.i9.2018.1222

Abstract

This paper presents an advanced particle swarm optimization Algorithm for solving the reactive power problem in power system. Bacterial Foraging Optimization Algorithm (BFOA) has recently emerged as a very powerful technique for real parameter optimization. In order to overcome the delay in optimization and to further enhance the performance of BFO, this paper proposed a new hybrid algorithm combining the features of BFOA and Particle Swarm Optimization (PSO) called advanced bacterial foraging-oriented particle swarm optimization (ABFPSO) algorithm for solving reactive power problem. The simulation results demonstrate good performance of the ABFPSO in solving an optimal reactive power problem. In order to evaluate the proposed algorithm, it has been tested on IEEE 57 bus system and compared to other algorithms.

Highlights

Main objective of optimal reactive power problem is to minimize the real power loss and bus voltage deviation
During the process of chemo taxis, the Bacterial Foraging Optimization Algorithm (BFOA) depends on random search directions which may lead to delay in reaching global solution
Shows the comparison of optimum results obtained from proposed methods with other optimization techniques. These results indicate the robustness of proposed approaches for providing better optimal solution in case of IEEE-57 bus system

Summary

Introduction

Main objective of optimal reactive power problem is to minimize the real power loss and bus voltage deviation. Many types of Evolutionary algorithms have been used to solve optimal reactive power flow problem [9,10] & some algorithms good in exploration & some better in exploitation alone. Proposed algorithm balances the exploration & exploitation in the search of global solution in optimal reactive power problem. Based on the researches on the foraging behavior of E-coli bacteria K.M. Passino proposed a new Evolutionary computation technique known as Bacterial. During the process of chemo taxis, the BFOA depends on random search directions which may lead to delay in reaching global solution. To reduce the time of optimization and to accelerate the convergence speed of group of bacteria near global optima for this BFO-PSO we propose a new hybrid algorithm "ABFPSO" in which the chemo tactic step had been made advanced. The performance of (ABFPSO) has been evaluated in standard IEEE 57 bus test system and the results analysis shows that our proposed approach outperforms all approaches investigated in this paper

Objective

Bacterial Foraging Optimization Algorithm

Hybrid of Bacterial Foraging Oriented with Particle Swarm Optimization

Elimination-dispersal

Conclusion