Comprehensive learning bat algorithm for optimal coordinated tuning of power system stabilizers and static VAR compensator in power systems. Issue 10 (2nd October 2020)

• Record Type: Journal Article
• Authors: Baadji, Bousaadia; Bentarzi, Hamid; Bakdi, Azzeddine
• Is Part Of: Engineering optimization. Volume 52:Issue 10(2020)
• Page Start: 1761
• Page End: 1779
• Publication Date: 2020-10-02
• Subjects: Power system stabilizer -- static VAR compensator -- comprehensive learning -- adaptive search strategy -- elitist learning; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2019.1677635
• Languages: English
• ISSNs: 0305-215X

Abstract

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 oscillations in multi-machine power systems considering a wide range of operating conditions. The CLBAT incorporates a new comprehensive learning strategy (CLS) to improve microbat cooperation; location updating is also improved to maintain the bats' diversity and to prevent premature convergence through a novel adaptive search strategy based on relative travelled distance. In addition, the proposed elitist learning strategy speeds up convergence during the optimization process and drives the global best solution towards promising regions. The superiority of the CLBAT over other algorithms is demonstrated via several experiments and comparisons through benchmark functions. The developed algorithm ensures convergence speed, credibility, computational resources and optimal tuning of PSSs and SVCs of multi-machine systems under different operating conditions through eigenanalysis, nonlinear simulation and performance indices.