Comprehensive learning bat algorithm for optimal coordinated tuning of power system stabilizers and static VAR compensator in power systems
| Autor: | Hamid Bentarzi, Azzeddine Bakdi, Bousaadia Baadji |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
021103 operations research
Control and Optimization Computer science Applied Mathematics 0211 other engineering and technologies Static VAR compensator 02 engineering and technology Management Science and Operations Research Industrial and Manufacturing Engineering Computer Science Applications Electric power system Control theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Bat algorithm |
| Zdroj: | Engineering Optimization. 52:1761-1779 |
| ISSN: | 1029-0273 0305-215X |
| DOI: | 10.1080/0305215x.2019.1677635 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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