Coordinated tuning of power system stabilizers using bio-inspired algorithms

Abstract This paper presents a comparison study among three bio-inspired optimization algorithms applied to solve the power system stabilizers tuning problem: Ant Colony Optimization, Bat Algorithm and Genetic Algorithm. The tuning procedure is formulated as an optimization problem which aims at minimizing the system damping taking into account a set of pre-specified operating scenarios. The proposed methodologies are applied to stabilizers tuning of the well-known New England test system.

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

[2]  E. G. M. de Lacerda,et al.  Capacitor Placement Using Ant Colony Optimization and Gradient , 2009 .

[3]  N. Martins,et al.  Coordinated Stabilizer Tuning in Large Power Systems Considering Multiple Operating Conditions , 2007, 2007 IEEE Power Engineering Society General Meeting.

[4]  Tamiru Alemu Lemma,et al.  Use of fuzzy systems and bat algorithm for exergy modeling in a gas turbine generator , 2011, 2011 IEEE Colloquium on Humanities, Science and Engineering.

[5]  Gerard Ledwich,et al.  Effects of various power system stabilizers on improving power system dynamic performance , 2013 .

[6]  Abdel Latif Elshafei,et al.  Damping inter-area modes of oscillation using an adaptive fuzzy power system stabilizer , 2010 .

[7]  T. C. Bora,et al.  Bat-Inspired Optimization Approach for the Brushless DC Wheel Motor Problem , 2012, IEEE Transactions on Magnetics.

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

[9]  L. Zanetta,et al.  An incremental approach to the coordinated tuning of power systems stabilizers using mathematical programming , 2005, IEEE Transactions on Power Systems.

[10]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[11]  M. L. Kothari,et al.  A self-tuning power system stabilizer based on artificial neural network , 2004 .

[12]  Mahmoud Moghavvemi,et al.  Optimization of power system stabilizers using participation factor and genetic algorithm , 2014 .

[13]  Glauco N. Taranto,et al.  Simultaneous tuning of power system damping controllers using genetic algorithms , 2000 .

[14]  M. Pai.  Energy function analysis for power system stability , 1989 .

[15]  E. S. Ali,et al.  A hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design , 2013 .

[16]  E. R. Chaparro,et al.  Coordinated tuning of a set of Static Var Compensators using Evolutionary Algorithms , 2011, 2011 IEEE Trondheim PowerTech.

[17]  Djalma M. Falcao,et al.  Robust decentralised control design using genetic algorithms in power system damping control , 1998 .

[18]  Bikash C. Pal,et al.  Robust and coordinated tuning of power system stabiliser gains using sequential linear programming , 2010 .

[19]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[20]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[21]  A. S. Al-Hinai,et al.  Dynamic stability enhancement using Particle Swarm Optimization Power System Stabilizer , 2009, 2009 2nd International Conference on Adaptive Science & Technology (ICAST).

[22]  D. K. Sambariya,et al.  Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm , 2014 .

[23]  Leonardo W. de Oliveira,et al.  Programming of thermoelectric generation systems based on a heuristic composition of ant colonies , 2013 .

[24]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[25]  E. S. Ali,et al.  Optimization of Power System Stabilizers using BAT search algorithm , 2014 .

[26]  D.M. Falcao,et al.  Tuning of generator excitation systems using meta-heuristics , 2006, 2006 IEEE Power Engineering Society General Meeting.

[27]  A. M. El-Zonkoly,et al.  Optimal tuning of power systems stabilizers and AVR gains using particle swarm optimization , 2006, Expert Syst. Appl..