基于遗传算法和神经网络的水电站生态流量优化运行模型

Qiyou Wang, Jiping Chen, Liang Cui
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摘要

鉴于水库河道生态流量调节与发电量最大水库的发电流量调节之间的矛盾,本文以最大发电量和最大平均生态保护度为目标函数,并利用遗传算法反向传播神经网络(GA-BP)算法建立了在水量平衡、库容、机组出力和机组溢流等约束条件下的水库流量调节模型、本文以最大发电量和最大平均生态保护度为目标函数,采用遗传算法反向传播神经网络(GA-BP)算法,建立了在水量平衡、库容、机组出力和机组溢流等约束条件下的水库流量调节模型,对调度方案进行了优化求解,并以疏勒河水电站为例对优化方案进行了验证。结果表明,优化后的调度模型能够保证生态流量需求,增加发电量 32680 kW-h,发电效益达 23.85%,为合理规划、调度和利用水库水资源提供了科学参考。
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Optimal Operation Model of Ecological Flow of Hydropower Station Based on Genetic Algorithm and Neural Network
In view of the contradiction between the ecological flow regulation of the reservoir River and the power generation flow regulation of the reservoir with the largest power generation, this paper takes the maximum power generation and the maximum average ecological protection degree as the objective function, and uses the genetic algorithm back propagation neural networks (GA-BP) algorithm to establish the reservoir flow regulation model under the constraints of water balance, reservoir capacity, unit output and unit overflow, The optimal solution of the dispatching scheme is carried out, and the Shulehe hydropower station is taken as an example to verify the optimal scheme. The results show that the optimized dispatching model can ensure the demand of ecological flow, increase 32680 kW·h power generation and 23.85% power generation benefit, so as to provide scientific reference for the rational planning, dispatching and utilization of reservoir water resources.
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