Velocity relaxed and craziness-based swarm optimized intelligent PID and PSS controlled AVR system
This paper explores a comparative performance study of two new classes of particle swarm optimization techniques, one with velocity update relaxation (VURPSO) and the other based on novel position, velocity updating strategy and craziness (CRPSO). Both VURPSO and CRPSO highly enhance searching ability. Genetic algorithm (GA) is considered for the sake of comparison. Finally, it is revealed that while applying in two power systems applications (PID controlled AVR system, PSS controlled AVR system), VURPSO exhibits better transient performance than CRPSO/GA. For on-line, off-nominal conditions, Takagi Sugeno fuzzy logic is applied to obtain on-line responses for both the system models.
There is a wide range of performance models being developed for the performance evaluation of parallel and distributed systems. A performance modelling approach described in this paper is based on a layered framework of the PACE methodology. With an initial implementation system, the model described by a performance specification language, CHIP/sup 3/S, can provide a capability for rapid calculation of relevant performance information without sacrificing accuracy of predictions. An example of the performance evaluation of an ASCI kernel application, Sweep3D, is used to illustrate the approach. The validation results on different parallel and distributed architectures with different problem sizes show a reasonable accuracy (approximately 12% error at most) can be obtained, allows cross-platform comparisons to be easily undertaken, and has a rapid evaluation time (typically less than 2s).