A fuzzy adaptive particle swarm optimization for Long-Term Optimal Scheduling of Cascaded hydropower station

A fuzzy adaptive particle swarm optimization (FAPSO) for optimal operation of cascaded hydropower station is presented to solve the shortcoming premature and easily local optimum of the standard particle swarm optimization (PSO). The fuzzy adaptive criterion is applied for inertia weight based on the evolution speed factor and square deviation of fitness for the swarm, in each iteration process, the inertia weight is dynamically changed using the fuzzy rules to adapt to nonlinear optimization process. The performance of FAPSO is demonstrated on cascaded hydropower station with 2 reservoirs, the comparison is drawn in PSO , FAPSO and dynamic programming (DP) in terms of the solution quality and computational efficiency. Simulation results show that the proposal approach has highest convergence speed and strong ability in global search.