%0 Journal Article
%T Particle Swarm Optimization and Bacterial Foraging Optimization Techniques for Optimal Current Harmonic Mitigation by Employing Active Power Filter
%A Sushree Sangita Patnaik
%A Anup Kumar Panda
%J Applied Computational Intelligence and Soft Computing
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/897127
%X Conventional mathematical modeling-based approaches are incompetent to solve the electrical power quality problems, as the power system network represents highly nonlinear, nonstationary, complex system that involves large number of inequality constraints. In order to overcome the various difficulties encountered in power system such as harmonic current, unbalanced source current, reactive power burden, active power filter (APF) emerged as a potential solution. This paper proposes the implementation of particle swarm optimization (PSO) and bacterial foraging optimization (BFO) algorithms which are intended for optimal harmonic compensation by minimizing the undesirable losses occurring inside the APF itself. The efficiency and effectiveness of the implementation of two approaches are compared for two different conditions of supply. The total harmonic distortion (THD) in the source current which is a measure of APF performance is reduced drastically to nearly 1% by employing BFO. The results demonstrate that BFO outperforms the conventional and PSO-based approaches by ensuring excellent functionality of APF and quick prevail over harmonics in the source current even under unbalanced supply. 1. Introduction Introduced by Kennedy and Eberhart in the year 1995 [1], particle swarm optimization (PSO) has emerged as a proficient stochastic approach of evolutionary computation. Since then it has been employed in various fields of applications and research and is successful in yielding an optimized solution. This algorithm mimics the social behavior executed by the individuals in a bird flock or fish school while searching for the best food location (global optima). The PSO algorithm neither depends upon the initial condition nor on the gradient information. Since it depends only on the value of objective function, it makes the algorithm computationally less expensive and much simple to implement. The low CPU and memory requirement is another advantage. However, some experimental results show that the local search ability around the optima is very poor though the global search ability of PSO is quite good [2¨C4]. This results in premature convergence in problems where multiple optima exist and; hence, the performance is degraded. The bacterial foraging optimization (BFO) proposed by Passino in the year 2002 [5] is based on natural selection that tends to eliminate animals with poor foraging strategies. After many generations, poor foraging strategies are eliminated while only the individuals with good foraging strategy survive signifying survival of the fittest.
%U http://www.hindawi.com/journals/acisc/2012/897127/