Evaluation strategy of particle swarm optimization and it’s application in pumping station system optimal operation

Abstract

To deal with the high energy consumption issue of existing pumping station operation modes, an optimization model ensuring the flow rate and safety demands of pumping station and aiming at the minimum operating power of the system, is established based on basic Particle Swarm Optimization (PSO), which can provide theoretical support for the optimal operation of pumping station. When solving high dimension problems, PSO is easy to get into local optimum and has a slow convergence rate at the later stage of iteration. In order to overcome the shortcomings mentioned above, the Halton sequence is used to generate the initial population randomly, at the same time the inertia weight reducing along the opening downward parabola and Simulated Annealing(SA) are employed, too. The Improved Hybrid Particle Swarm Optimization (IHPSO) is implemented in Matlab. And it is applied to calculate the optimization schemes of the pumping station system of the South-to-North Water Transfer Project in China. The results show that the convergence and stability of the IHPSO are better than the PSO, and IHPSO can effectively solve pumping station optimization operation with multiple variables. The power of the optimum scheme based on IHPSO is about 7.68% less than that based on PSO, and it is decreased about 11.51% compared with the designed scheme.