A multi objective vector evaluated improved honey bee mating optimization for optimal and robust design of power system stabilizers

Abstract This paper presents the Parallel Vector Evaluated Improved Honey Bee Mating Optimization (VEIHBMO) as a novel multi objective technique to obtain a set of optimal Power System Stabilizers (PSSs) parameters. It includes a feedback signal of a remote machine and local and remote input signal ratios for each machine in a multi-machine power system under various operating conditions. This goal is formulated as a multi objective optimization process with two competing and non-commensurable fitness functions. The objectives considered in this paper are the time domain based integral square time of square error (ISTSE) and eigenvalues based comprising the damping factor. The effectiveness of the proposed method is demonstrated on 4-machine two-area and 16-machine five-area power system under different loading conditions. The system performance is assessed through the time multiplied absolute value of the error (ITAE), eigenvalues and figure of demerit (FD) analysis performance indices. The simulation results show that the performance of the proposed stabilizer is comparable to that which could be obtained by the conventional design, but without the need for the estimation and computation of the external system parameters.

[1]  Amin Khodabakhshian,et al.  Multi-machine power system stabilizer design by using cultural algorithms , 2013 .

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

[3]  Hong He,et al.  Task allocation for maximizing reliability of distributed computing systems using honeybee mating optimization , 2010, J. Syst. Softw..

[4]  João Peças Lopes,et al.  A neural power system stabilizer trained using local linear controllers in a gain-scheduling scheme , 2005 .

[5]  Hossein Shayeghi,et al.  Robust design of multimachine power system stabilizers using fuzzy gravitational search algorithm , 2013 .

[6]  M. Mary Linda,et al.  A new-fangled adaptive mutation breeder genetic optimization of global multi-machine power system stabilizer , 2013 .

[7]  Kwang Y. Lee,et al.  Multi-objective based on parallel vector evaluated particle swarm optimization for optimal steady-state performance of power systems , 2009, Expert Syst. Appl..

[8]  Gary B. Lamont,et al.  Considerations in engineering parallel multiobjective evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[9]  Behrooz Vahidi,et al.  Improvement of low frequency oscillation damping by allocation and design of power system stabilizers in the multi-machine power system , 2013 .

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

[11]  Fahmy M. Bendary,et al.  Robust decentralized PID-based power system stabilizer design using an ILMI approach , 2010 .

[12]  Ali Ghasemi,et al.  A fuzzified multi objective Interactive Honey Bee Mating Optimization for Environmental/Economic Power Dispatch with valve point effect , 2013 .

[13]  Ji-Pyng Chiou,et al.  Parameters tuning of power system stabilizers using improved ant direction hybrid differential evolution , 2009 .

[14]  A. H. Coonick,et al.  Coordinated synthesis of PSS parameters in multi-machine power systems using the method of inequalities applied to genetic algorithms , 2000 .

[15]  Omid Bozorg Haddad,et al.  Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization , 2006 .

[16]  Ali Ajami,et al.  A Multi-Objective HBMO-Based New FC-MCR Compensator for Damping of Power System Oscillations , 2007 .

[17]  Konstantinos E. Parsopoulos,et al.  MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION , 2003 .

[18]  Taher Niknam,et al.  A new honey bee mating optimization algorithm for non-smooth economic dispatch , 2011 .

[19]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..