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A particle swarm optimization algorithm based collaborative optimal scheduling for multi-level water basin in non-flood season

机译:基于粒子群优化算法的非汛期多层流域协同最优调度

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Water resource optimization has become an important issue due to water shortage in recent years. In the paper, we build a collaborative model for optimal water scheduling. It considers many real factors in scheduling reasonably, including domestic water, ecological water, production water, agricultural water and their backwater et al. In the model, all the factors determine the minimum required water and control section flow in river. When a water control system monitors the fact that water flow in control section is lower than the minimum required water flow. The multi-reservoirs water dispatching which based on collaborative particle swarm optimization is activated to solve the contradiction among different water demanding, especially in the peak of agricultural water in non-flood. The experiment result shows that the model in practice is reliable.
机译:近年来,由于水资源短缺,水资源优化已成为一个重要问题。在本文中,我们建立了一个用于优化水调度的协作模型。它合理地考虑了许多实际因素,包括生活用水,生态用水,生产用水,农业用水及其回水等。在模型中,所有因素决定了河流的最低需水量和控制断面流量。当水控制系统监视以下事实时,控制部分的水流量低于所需的最小水流量。激活了基于协同粒子群算法的多水库调度,解决了不同需水量之间的矛盾,特别是在非洪水农业用水高峰期。实验结果表明该模型是可靠的。

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