基于遗传算法和神经网络的泵站经济运行研究

以泵站总耗能最小为目标，建立了叶片可调节泵站站间和站内经济运行优化数学模型，采用遗传算法应用Matlab语言实现优化计算。对江都第4站1999年实际运行资料进行优化计算，总消耗功率比经验操作可减小3．39％左右。针对泵站的流量和扬程变化频繁而一般的优化计算方法速度较慢的问题，以仿真优化结果为样本，利用人工神经网络对相似工况进行预测，预测结果平均误差为1．99％。遗传算法和神经网络联合应用，求解精度和可靠性较高，是解决泵站优化运行问题的有效方法。

Optimal Operation of Pumping Stations Based on Genetic Algorithms and Artificial Neural Networks

Aimed at the minimal energy consumption, the mathematical model for adjustable-blade pumps or pumping stations was established. Using the method of genetic algorithms and Matlab, the data of Jiangdu drainage and irrigation station were studied, the results showed that 3.39% energy could be save comparing with its actual energy consumed in the same year. Because the flowrate and head of pump station was changing frequently and the conventional optimal calculation method often takes long time, the author proposed artificial neural networks model to predict movement patterns of the Jiangdu first pumping station, the result of optimal operation is used as sample. By combination with the genetic algorithms and artificial neural networks, the satisfactory result can be obtained. The new method is not only simple and easy to implement, but also has high accuracy and reliability, it is an effective way for solving the optimization problem in pump station .