摘要
为有效解决小浪底-西霞院水库群在调度过程中因泥沙淤积以及黄委调度指令导致发电计划制订困难的问题,构建考虑泥沙淤积的小浪底-西霞院水库群优化调度模型,针对调度模型特点,一方面,选取影响水库有效库容的因子,并结合人工神经网络求解出不同时期、不同水位下的有效库容,另一方面,提出POA-DP算法实现调度模型的求解。通过实例分析表明人工神经网络能够有效表征泥沙淤积对水位—有效库容关系的影响,并能够预测出未来的水位—有效库容关系发展趋势,所构建的水库群优化调度模型能够在满足黄委调度指令情况下实现发电效益最大化。
In order to effectively solve the problem of difficulty in making power generation plan in Xiaolangdi-Xixiayuan reservoirs due to sediment deposition and the influence of Yellow River Conservancy Conservancy Commission's regulation order in the process of operation,an optimal operation model of Xiaolangdi-Xixiayuan cascade reservoirs considering sediment deposition was constructed by the paper.According to the characteristics of the operation model,the factors affecting the reservoir capacity were selected and the effective reservoir capacity at different stages and different water levels was derived by artificial neural network.On the other hand,POA-DP algorithm was proposed to solve the model.The results show that the artificial neural network can effectively characterize the influence of sediment deposition on water level-reservoir capacity and effectively predict the future trend of water level-reservoir capacity.The optimal operation model of reservoir group can maximize the power generation under the conditions of meeting Yellow River Conservancy Commission's operation order.
作者
沈笛
赵珂
王渤权
王建平
董泽亮
SHEN Di;ZHAO Ke;WANG Boquan;WANG Jianping;DONG Zeliang(Nanjing Nari Water Resources and Hydropower Technology Company,Nanjing 211100,China;Xiaolangdi Multipurpose Project Construction and Management Bureau,MWR,Zhengzhou 450000,China)
出处
《人民黄河》
CAS
北大核心
2020年第12期23-28,共6页
Yellow River
基金
小浪底调度自动化系统开发与系统集成项目(2017-00135ZQ-SDGS)。
关键词
泥沙淤积
人工神经网络
水位—有效库容
优化调度
小浪底
西霞院
sediment deposition
artificial neural network
water level-reservoir capacity
optimal operation
Xiaolangdi
Xixiayuan
