Abstract
This paper explores a comparative performance study of two new classes of particle swarm optimization (PSO) techniques and binary coded genetic algorithm (GA) applied to the optimization of proportional-integral-derivative (PID) gains of PID-controlled automatic voltage regulator (AVR). The two novel swarm optimization techniques are velocity update relaxation particle swarm optimization (VURPSO) and craziness based particle swarm optimization (CRPSO). Incorporation of velocity-updating relaxation strategy in conventional PSO reduces computational effort and enhances searching ability in VURPSO. Enhanced searching ability in normal PSO is also observed in CRPSO by inclusion of a new velocity updating strategy and craziness. In comparative study, it has been revealed that VURPSO exhibits better transient performance than CRPSO. GA yields suboptimal results. For on-line, off-nominal system operating conditions Takagi Sugeno fuzzy logic (TSFL) has been successfully applied to obtain on-line responses.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
