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Particle Swarm Optimization in Reservoir Flood Dispatching Research

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Tutor: KangLing
School: Huazhong University of Science and Technology
Course: Hydrology and Water Resources
Keywords: Flood Optimal Scheduling,PSO,Three Gorges Reservoir,Chenglingji
CLC: TP301.6
Type: Master's thesis
Year:  2011
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China's Yangtze River floods frequently, while the middle region is the focus of flood protection . The middle reaches of the Yangtze River Three Gorges Reservoir flood obvious, but the scheduling mode or to schedule a graph and scheduling rules based on regular schedule , can not fully play its role in flood control . In this paper, the particle swarm optimization algorithm is applied to the Three Gorges Reservoir flood compensation Chenglingji station scheduling optimization model to improve the effect of the Three Gorges Reservoir flood . Particle swarm optimization algorithm is a simple and practical global optimization algorithm of swarm intelligence . Since the algorithm has been proposed in various engineering fields have been widely used. In the field of reservoir optimal operation scheduling has some applied research . But there are basic particle swarm algorithm prematurity , slow convergence and other defects. This paper presents an adaptive particle swarm algorithm , particle swarm algorithm for a related improvements to increase their ability to escape from local optima and applied to the design flood Hydrograph , Muskingum flood routing model parameter optimization the solving . In this paper, for the great flood of 1954 data , the establishment of the Three Gorges flood compensation for Chenglingji scheduling optimization model, reservoir operation constraints and the Three Gorges Reservoir specific scheduling rules of integration, in order to minimize the amount Chenglingji station diversion target . Using adaptive particle swarm algorithm to solve the optimization model . The results showed that the amount of flood Chenglingji station is small, the effect is more obvious optimization for Reservoir Flood Optimal Scheduling Model provides a new method for solving . For this they need to focus on the actual scheduling forecast issue, the use of artificial neural networks to establish short-term forecasting model Chenglingji outbound traffic , and its related testing and results analysis, pointing out the deficiencies of the model , and make improvements .
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