Application of improved particle swarm optimization algorithm to operation of hydropower station group

A new strategy of inertia weight coefficient for particle swarm optimization (PSO) algorithm, namely self-adaptive exponential inertia weight coefficient (SEIWC), is proposed to solve the problems of prematurity, slow convergence and falling into local minimum, which may happen to the application of conventional particle swarm optimization algorithm using linearly increasing inertia weight coefficient. The concepts of crossover and mutation of chromosome are introduced to improve the global searching ability of PSO algorithm. The effectiveness of the proposed algorithm is verified using the Rosenbrock function and Schaffer function. The improved algorithm is applied to formulate the optimal operation of hydropower station group in the Minjiang River, Fujian Power Grid, with maximum energy regarded as the objective function. The result indicates that the improved algorithm is much better than the traditional algorithm and is comparable with that of progressive optimization algorithm.