Comprehensive learning bat algorithm for optimal coordinatedtuning of power system stabilizers and static VAR compensator inpower systems

Abstract

This article presents a novel comprehensive learning bat algorithm (CLBAT)for the optimal coordinated design of power system stabilizers (PSSs)and static VAR compensator (SVC) for damping electromechanical oscilla-tions in multi-machine power systems considering a wide range of oper-ating conditions. The CLBAT incorporates a new comprehensive learningstrategy (CLS) to improve microbat cooperation; location updating is alsoimproved to maintain the bats’ diversity and to prevent premature con-vergence through a novel adaptive search strategy based on relative trav-elled distance. In addition, the proposed elitist learning strategy speedsup convergence during the optimization process and drives the globalbest solution towards promising regions. The superiority of the CLBATover other algorithms is demonstrated via several experiments and com-parisons through benchmark functions. The developed algorithm ensuresconvergence speed, credibility, computational resources and optimal tun-ing of PSSs and SVCs of multi-machine systems under different operatingconditions through eigenanalysis, nonlinear simulation and performanceindices.