Application of a Fuzzy Programming Through Stochastic Particle Swarm Optimization to Assessment of Filter Management Strategies in Fluid Power System Under Uncertainty

Abstract

A fuzzy programming through stochastic particle swarm optimization is developed for the assessment of filter allocation and replacement strategies in fluid power system (FPS) under uncertainty. It can not only handle uncertainties expressed as L-R fuzzy numbers but also enhance the system robustness by transforming the fuzzy inequalities into inclusive constraints. As the simulation results indicate, the developed model can successfully decrease the total cost and enhanced the safety of system. Generally, it is believed that the model can help identify excellent filter allocation and replacement strategy with minimized operation cost and system failure risk while protecting the system.