Particle Swarm Optimization and Bacterial Foraging Optimization Techniques for Optimal Current Harmonic Mitigation by Employing Active Power Filter

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]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  S. Żak.  Systems and control , 2002 .

[3]  Sukumar Mishra,et al.  A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation , 2005, IEEE Transactions on Evolutionary Computation.

[4]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[5]  Kamal Al-Haddad,et al.  A review of active filters for power quality improvement , 1999, IEEE Trans. Ind. Electron..

[6]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[7]  E.R. Cadaval,et al.  Comparison of Control Strategies for Shunt Active Power Filters in Three-Phase Four-Wire Systems , 2007, IEEE Transactions on Power Electronics.

[8]  C. N. Bhende,et al.  Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation , 2007, IEEE Transactions on Power Delivery.

[9]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[10]  Francesco Grimaccia,et al.  Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks , 2010, Appl. Comput. Intell. Soft Comput..

[11]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[12]  Gil D. Marques,et al.  An instantaneous active and reactive current component method for active filters , 2000 .