水电站引水渠水沙智能监测与预警系统应用研究  

Applied Studies on Water and Sediment Intelligent Monitoring and Early Warning System for Hydropower Station Diversion Channel

作　　者：王冉旋 马仲坤 陈娜[2] 周聂 陈华[2] 马志军 WANG Ranxuan;MA Zhongkun;CHEN Na;ZHOU Nie;CHEN Hua;MA Zhijun(Xinjiang Jilintai Hydropower Development Co.,Ltd.,China Energy,Yili 835100,China;State Key Laboratory of Water Resources Engineering and Management,Wuhan University,Wuhan 430072,China)

机构地区：[1]国家能源集团新疆吉林台水电开发有限公司,新疆伊犁835100 [2]武汉大学水资源工程与调度全国重点实验室,湖北武汉430072

出　　处：《人民黄河》2025年第4期133-140,共8页Yellow River

基　　金：国家重点研发计划项目(2022YFC3002701)。

摘　　要：为减小水轮机泥沙磨损影响、提高水电站效益,设计开发了水电站引水渠水沙智能监测与预警系统,采用残差神经网络模型(ResNet50_v2)和图像法实现水沙同步在线智能监测,采用长短期记忆网络(LSTM)模型预测未来5 h的含沙量,通过损益平衡分析确定最佳含沙量预警阈值和适宜的预警机制,以最大化减少发电损失。以喀什河流域塔勒德萨依水电站为例,进行系统实际应用,应用结果表明:系统监测流量与ADCP测流设备实测值的E_(MA)≤2.97 m^(3)/s、E_(MR)≤2.17%;系统监测含沙量与人工烘干法、光学测沙仪实测值的E_(MA)≤0.20 kg/m^(3)、E_(MR)≤16.91%;LSTM模型对5 h预见期预测含沙量的E_(NS)>0.7,模型总体监测与预测精度较高;水电站含沙量预警阈值为3.59 kg/m^(3),通过精准测报与科学预警,可规避发电设备泥沙磨损事故,节省维修费用,提高综合效益。In order to mitigate the adverse effects of turbine sediment abrasion and enhance the operational efficiency of hydroelectric plants,an intelligent monitoring and early warning system for water and sediment in the diversion channel of hydroelectric power stations was de-signed and developed,the image-based method was employed in conjunction with the Residual Neural Network model(ResNet50_v2)to fa-cilitate the synchronous online intelligent monitoring of water and sediment,the Long Short Term Memory(LSTM)model was employed to forecast the sediment concentration for the next 5 h.Determined the optimal sediment concentration warning threshold and appropriate warning mechanism through profit and loss balance analysis to minimize power generation losses.Taking the Talade Sayi Hydropower Station in the Kashgar River Basin as an example,the system was applied in practice.The application results show that the system monitors the flow rate and the measured values of ADCP flow measurement equipment,with E MA≤2.97 m^(3)/s、E MR≤2.17%.The system monitors the sediment concentration and the measured values of manual drying method and optical sand analyzer,with E MA≤0.20 kg/m^(3)、E MR≤16.91%.The LSTM model has anE NS>0.7 for predicting sediment concentration during a 5 hours forecast period,indicating high overall monitoring and prediction accuracy of the model.The threshold for sediment concentration warning in hydropower stations is 3.59 kg/m^(3).Through precise measurement and scientific warning,it is possible to avoid sediment wear accidents in power generation equipment,save maintenance costs,and improve overall efficiency.

关 键 词：泥沙磨损 图像法 智能监测 含沙量预警阈值 系统 塔勒德萨依水电站 

分 类 号：TV732[水利工程—水利水电工程]