Application Research On Particle Swarm Optimization In The Optimal Operation Of Reservoir Flood Control

Flood is frequent in Yangtze River basin, and the middle reach is the most important area. The Three Gorges Reservoir has an important effect in flood control of this area, but the operation mode still relies on regulation diagram and operation rules. This cannot maximize the effsct. This paper applies the Particle Swarm Optimization (PSO) to the Three Gorges Reservoir compensation operation to decreas flood divertion to the Chenglingji station.PSO is a new, simple and praetieal optimization algorithm based on swarm intelligence. Since proposed, PSO has been widely used in various engineering fields including reservoir operation optimization. However, the classical PSO still has its inherent flaws, like fall into local optimum and low computing speed. This paper proposed an Improved Adaptive Particle Swarm Optimization (IA-PSO) to improve its ability of jumping out from local optimum.Then use it to solve the design flood flow curve, flood routing Muskingum parameter optimization model.Using the flood in 1954 as an example, this paper establishs the Three Gorges Reservoir compensation operation on Chenglingji station model, which combined the constraint conditions of the Three Gorges Reservoir and its operation rules in target of minimum flood volume of Chenglingji station.The IA-PSO is used to solve the model. The results show that the flood volume diverted to Chenglingji station is decrease, and the effect of the optimization method is obvious.It provides a new way to solve the optimal operation models.This paper also proposes a short-term inflow forecasting model of Chenglingji station by artificial neural network method, which is important on the flood real-time operation.Then pointed out the shortcomings of the model and make some improvements of this model based on the tests and results.