非弃水期葛洲坝水电站下游水位变化过程预测新方法

由于现有的水电站下游水位预测方法计算误差较大,选择葛洲坝水电站为研究对象,提出一种新的电站在不弃水情况下的下游水位变化过程预测方法。该方法基于BP神经网络算法,利用水电站监控数据实现电站下游水位的高精度预测,满足电站实时调度需求。对比现有的水位流量曲线查值法和非恒定流经验公式法,该方法有如下优势:①无需采用出库流量进行预测,避免了流量计算误差的影响;②建模过程中考虑了下游水位变化"后效性"影响,大幅提升水电站调峰时的预测精度;③可直接计算下游水位变化过程,计算结果稳定,精度更高,尤其在非弃水期葛洲坝水电站大调峰工况下,预测精度明显提高。

A novel forecasting method for downstream water level variation of Gezhouba Hydropower Station during non-abandoning water period

Since the existing methods of water level forecasting in the downstream of a hydropower station have a larger computational error, Gezhouba Hydropower Station was taken as a research object to establish a novel method for downstream water level variation forecasting during the non-abandoning water period. Based on the BP neural network and the hydropower station monitoring data, the downstream water level forecasting with high accuracy was realized, which can satisfy the needs of real-time scheduling. Compared with the methods of water level-discharge relationship and the empirical formula for unsteady flows, the present forecasting method has the following advantages: (1)It does not use the storage outflow data to calculate the water level, and the influence of the outflow calculation error can be avoided; (2)The hysteretic nature effects of the downstream water level variation is considered during the model construction process, so the forecast accuracy during the hump modulation periods can be greatly improved; (3)It can forecast the downstream water level changing process directly with stable calculation results and higher calculation accuracy, which can significantly improve the forecast accuracy under large peak shaving operating conditions of Gezhouba Hydropower Station during the non-abandoning water period.