摘要
为降低水电站长期运行过程中频繁的无规律动作对于水头高、库容小、调节性能差的水电站造成的损害,最大限度利用水头优势增发电量,提高水电站运行的效益性和安全性,提出了一种机理与数据混合驱动的水位预测方法。该方法通过PSO(Particle Swarm Optimization)算法优化耦合BP(Back Propagation)神经网络和水量平衡模型,其中,数据驱动模型提供基准值,水量平衡机理模型修正水位趋势的合理性;将该方法应用于沙坪二级水电站的水位预测,对比分析水量平衡模型、BP神经网络模型和耦合模型预测结果。结果表明:提出的耦合模型有效避免了机理模型的累积误差和数据驱动的反常性;相对于水量平衡模型和BP神经网络模型,该耦合模型具有较高的预测精度和实用性,其平均绝对百分比误差MAPE和拟合优度R 2分别为0.0013和0.97,预测幅度更贴近真实水位。研究成果可为水电站面对短期的水位变化提前做出反应提供理论依据。
In order to reduce the damage caused by frequent irregular action to the hydropower stations of high water head,small storage capacity and poor regulation performance in the long-term operation process,maximize the use of the water head advantage to increase power production,and improve the efficiency and safety of hydropower station operation,a water level prediction method driven by mechanism and data is proposed.In this method,Back Propagation(BP)neural network and water balance mechanism are coupled with Particle Swarm Optimization(PSO)algorithm,in which the data-driven model provides the reference value and the water balance mechanism model corrects the rationality of water level trend.This method is applied to the water level prediction of ShapingⅡHydropower Station,and the prediction results of water balance prediction model,BP neural network prediction model and coupled model are compared and analyzed.The results show that the proposed coupled model effectively avoids the accumulation error of the mechanism model and the un-constancy of the data-drive model.Compared with the water balance prediction model and the BP neural network prediction model,the coupled model has higher prediction accuracy and practicability.The mean absolute percentage error and goodness of fit are 0.0013 and 0.97,respectively,and the prediction amplitude is closer to the real water level.The research results can provide theoretical basis for hydropower station to respond in advance to short-term water level changes.
作者
张钰彬
练继建
王孝群
封天雨
ZHANG Yubin;LIAN Jijian;WANG Xiaoqun;FENG Tianyu(School of Water Conservancy and Hydroelectric Power,Hebei University of Engineering,Handan 056038,China;Key Laboratory of Intelligent Water Resources of Hebei Province,Hebei University of Engineering,Handan 056038,China)
出处
《人民长江》
北大核心
2023年第3期90-95,共6页
Yangtze River
基金
国家自然科学基金联合基金重点支持项目(U20A20316)
河北省自然科学基金项目(E2020402074)。
关键词
水位预测
水量平衡
BP神经网络
PSO算法
沙坪二级水电站
water level prediction
water balance
BP neural network
PSO algorithm
ShapingⅡHydropower Station
