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A platform for research: civil engineering, architecture and urbanism
Simulation operation method for large-scale reservoir group in main stream and tributaries of river basin
The invention discloses a simulation operation method for a large-scale reservoir group in a main stream and tributaries of a river basin, and belongs to the field of optimal operation of a hydropowersystem. The method comprises the following steps that (1) construction of reservoir operation function is conducted, analysis of relevant factors affecting reservoir outflow is conducted, correlationanalysis is conducted, and determination of input factors of each reservoir operation function is conducted; (2) construction of a neural network model is conducted according to the input factors ofthe operation function, optimization of neural network parameters is conducted by adopting an adaptive moment estimation algorithm, training is conducted on the constructed neural network by using historical operation data of the reservoir, and the trained neural network is used as fitting function of the reservoir operation function; and (3) according to the fitting function of the reservoir operation function, a spatial topological structure and the constraints of the reservoir operation, the simulation operation model of the reservoir group is established to simulate the operation process of the basin reservoir group step by step. By means of the simulation operation method, the fitting accuracy is improved significantly, and the operation plan of the large-scale reservoir group in themain stream and tributaries of the river basin can be more accurately described in the case of the unknown operation law.
本发明公开了一种流域干支流大规模水库群模拟调度方法，属于水电系统优化调度领域。包括：(1)构建水库调度函数，分析影响水库出库流量的相关因素，进行相关性分析，确定各水库调度函数的输入因子；(2)根据所述调度函数的输入因子，构建神经网络模型，并采用自适应矩估计算法对神经网络参数进行寻优，利用水库历史运行数据对所构建的神经网络进行训练，训练好的神经网络作为水库调度函数的拟合函数；(3)根据所述水库调度函数的拟合函数、空间拓扑结构和水库运行约束条件建立水库群仿真调度模型，逐级模拟流域水库群调度运行过程。本发明显著提高了拟合精度，能更准确的描述调度计划未知情况下流域干支流大规模水库群运行规律。
Simulation operation method for large-scale reservoir group in main stream and tributaries of river basin
The invention discloses a simulation operation method for a large-scale reservoir group in a main stream and tributaries of a river basin, and belongs to the field of optimal operation of a hydropowersystem. The method comprises the following steps that (1) construction of reservoir operation function is conducted, analysis of relevant factors affecting reservoir outflow is conducted, correlationanalysis is conducted, and determination of input factors of each reservoir operation function is conducted; (2) construction of a neural network model is conducted according to the input factors ofthe operation function, optimization of neural network parameters is conducted by adopting an adaptive moment estimation algorithm, training is conducted on the constructed neural network by using historical operation data of the reservoir, and the trained neural network is used as fitting function of the reservoir operation function; and (3) according to the fitting function of the reservoir operation function, a spatial topological structure and the constraints of the reservoir operation, the simulation operation model of the reservoir group is established to simulate the operation process of the basin reservoir group step by step. By means of the simulation operation method, the fitting accuracy is improved significantly, and the operation plan of the large-scale reservoir group in themain stream and tributaries of the river basin can be more accurately described in the case of the unknown operation law.
本发明公开了一种流域干支流大规模水库群模拟调度方法，属于水电系统优化调度领域。包括：(1)构建水库调度函数，分析影响水库出库流量的相关因素，进行相关性分析，确定各水库调度函数的输入因子；(2)根据所述调度函数的输入因子，构建神经网络模型，并采用自适应矩估计算法对神经网络参数进行寻优，利用水库历史运行数据对所构建的神经网络进行训练，训练好的神经网络作为水库调度函数的拟合函数；(3)根据所述水库调度函数的拟合函数、空间拓扑结构和水库运行约束条件建立水库群仿真调度模型，逐级模拟流域水库群调度运行过程。本发明显著提高了拟合精度，能更准确的描述调度计划未知情况下流域干支流大规模水库群运行规律。
Simulation operation method for large-scale reservoir group in main stream and tributaries of river basin
一种流域干支流大规模水库群模拟调度方法
ZHOU JIANZHONG (author) / LUO GUANGLEI (author) / DAI LING (author) / LU CHENGWEI (author) / FENG ZHONGKAI (author) / JIANG ZHIQIANG (author) / ZHA GANG (author) / ZENG YU (author) / ZHU SIPENG (author) / QIU HONGYA (author)
2020-04-10
Patent
Electronic Resource
Chinese
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