Comprehensive learning bat algorithm for optimal coordinated tuning of power system stabilizers and static VAR compensator in power systems

• Published in: Engineering optimization Vol. 52; no. 10; pp. 1761 - 1779
• Main Authors: Baadji, Bousaadia, Bentarzi, Hamid, Bakdi, Azzeddine
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.10.2020; Taylor & Francis Ltd
• Subjects: adaptive search strategy; Adaptive search techniques; Algorithms; comprehensive learning; Computer simulation; Convergence; Damping; elitist learning; Machine learning; Optimization; Performance indices; Power system stabilizer; static VAR compensator; Strategy; Tuning
• ISSN: 0305-215X; 1026-745X; 1029-0273; 1029-0273
• DOI: 10.1080/0305215X.2019.1677635

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.