三峡-葛洲坝梯级电站的水位预测关系到电站安全稳定运行和综合效益发挥,然而在动静库容计算体系转换关系复杂、电站下游非恒定流等多种因素的综合影响下,传统方法在短期水位预测过程时难以跟踪,在电站承担调峰、调频任务及复杂工况下有突破调度规程及开闸的风险,从而引发工程安全风险和经济损失。采用长短时记忆网络(LSTM)深度学习方法,建立了三峡-葛洲坝梯级电站超短期水位预测模型,利用水位、入库流量、出力数据预测电站超短期的水位过程,并通过大调峰工况数据对模型预测精度进行应用分析。研究结果表明该模型总体精度较高、稳定性和适应性较好,在不同调峰工况下预测精度稳定,但在水位极值处预测结果往往会出现均化现象。三峡、葛洲坝上游水位24 h预测平均误差均<0.05 m。研究成果可为梯级电站精细化调度提供技术支撑。

Water level prediction for the Three Gorges-Gezhouba cascade hydropower stations is crucial for their safe and stable operation and overall benefits. Nevertheless, due to the combined effects of multiple factors, such as the complex transformation between dynamic and static storage-capacity calculations and the unsteady flow downstream of the stations, traditional methods struggle to accurately predict short-term water levels. When the stations perform peak-shaving and frequency-regulation tasks under complex operating conditions, there is a risk of violating scheduling regulations and opening the gates, which may lead to engineering safety hazards and economic losses. In this study, we employed the Long Short-Term Memory (LSTM) deep-learning method to develop an ultra-short-term water-level prediction model for the Three Gorges-Gezhouba Hydropower Stations. We utilized water-level, inflow, and output data to forecast the ultra-short-term water-level processes of the stations. Subsequently, we analyzed the prediction accuracy of the model using data from peak-shaving scenarios. The results show that the model exhibits high overall accuracy, stability, and adaptability, and maintains stable prediction accuracy under different peak-shaving conditions. However, the prediction results tend to be homogenized at extreme water levels. The average error of 24-hour water-level prediction for the upstream of the Three Gorges and Gezhouba is less than 0.05 m. These findings can offer technical support for the refined scheduling of cascade hydropower stations.