A hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design

Abstract A novel hybrid approach involving Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization Algorithm (BFOA) called Bacterial Swarm Optimization (BSO) is illustrated for optimal Power System Stabilizers (PSSs) design in a multimachine power system. In BSO, the search directions of tumble behavior for each bacterium are oriented by the individual’s best location and the global best location of PSO. The proposed hybrid algorithm has been extensively compared with the original BFOA algorithm and the PSO algorithm. Simulation results have shown the validity of the proposed BSO in tuning PSSs compared with BFOA and PSO. Moreover, the results are presented to demonstrate the effectiveness of the proposed controller to improve the power system stability over a wide range of loading conditions and various disturbances.

[1]  Mohammad Ali Abido,et al.  Robust tuning of power system stabilizers in multimachine power systems , 2000 .

[2]  Wael Mansour Korani Bacterial foraging oriented by Particle Swarm Optimization strategy for PID tuning , 2009, 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA).

[3]  S. M. Abd-Elazim,et al.  Coordinated design of PSSs and SVC via bacteria foraging optimization algorithm in a multimachine power system , 2012 .

[4]  K. Sebaa,et al.  Optimal Locations and tuning of Robust Power System Stabilizers using Genetic Algorithms , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[5]  Ajith Abraham,et al.  Synergy of PSO and Bacterial Foraging Optimization - A Comparative Study on Numerical Benchmarks , 2008, Innovations in Hybrid Intelligent Systems.

[6]  R. Asgharian A robust H/sup /spl infin// power system stabilizer with no adverse effect on shaft torsional modes , 1994 .

[7]  P. Kundur,et al.  Application of Power System Stabilizers for Enhancement of Overall System Stability , 1989, IEEE Power Engineering Review.

[8]  Azah Mohamed,et al.  An efficient particle swarm optimization technique with chaotic sequence for optimal tuning and placement of PSS in power systems , 2012 .

[9]  J. Nanda,et al.  Multi-machine power system stabilizer design by rule based bacteria foraging , 2007 .

[10]  H. Happ Power system control and stability , 1979, Proceedings of the IEEE.

[11]  Huibert Kwakernaak,et al.  Robust control and H∞-optimization - Tutorial paper , 1993, Autom..

[12]  Siti Mariyam Shamsuddin,et al.  Particle Swarm Optimization: Technique, System and Challenges , 2011 .

[13]  M. A. Abido,et al.  Optimal multiobjective design of robust power system stabilizers using genetic algorithms , 2003 .

[14]  Mathukumalli Vidyasagar,et al.  Robust controllers for uncertain linear multivariable systems , 1984, Autom..

[15]  Peter W. Sauer,et al.  Power System Dynamics and Stability , 1997 .

[16]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[17]  S. Panda Multi-objective PID controller tuning for a FACTS-based damping stabilizer using Non-dominated Sorting Genetic Algorithm-II , 2011 .

[18]  Narayana Prasad Padhy,et al.  Robust Power System Stabilizer Design Using Particle Swarm Optimization Technique , 2008 .

[19]  M. A. El-Sharkawy,et al.  Design and allocation of power system stabilizers using the particle swarm optimization technique for an interconnected power system , 2012 .

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

[21]  V. Selvi,et al.  Comparative Analysis of Ant Colony and Particle Swarm Optimization Techniques , 2010 .

[22]  P. Kundur,et al.  Power system stability and control , 1994 .

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

[24]  M. A. Abido,et al.  Pole placement technique for PSS and TCSC-based stabilizer design using simulated annealing , 2000 .

[25]  R. Jayapal,et al.  Real time implementation of H ∞ loop shaping robust PSS for three-machine power system using dSPACE , 2013 .

[26]  Amin Safari,et al.  A robust PSSs design using PSO in a multi-machine environment , 2010 .

[27]  M. A. Abido Optimal des'ign of Power System Stabilizers Using Particle Swarm Opt'imization , 2002, IEEE Power Engineering Review.

[28]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[29]  S. M. Holzer,et al.  Book Reviews : SYSTEM DYNAMICS Katsuhiko Ogata Prentice-Hall, Inc., Englewood Cliffs, NJ, 1978 , 1980 .

[30]  M. A. Abido,et al.  Robust design of multimachine power system stabilizers using simulated annealing , 2000 .

[31]  M. A. Abido,et al.  Simultaneous stabilization of multimachine power systems via genetic algorithms , 1999, IEEE Transactions on Power Systems.

[32]  T. C. Yang,et al.  Applying H∞ optimisation method to power system stabiliser design part 1: single-machine infinite-bus systems , 1997 .

[33]  E. S. Ali,et al.  Bacteria foraging optimization algorithm based load frequency controller for interconnected power system , 2011 .