水泵水轮机全特性的集成PSO_BP神经网络模型

PSO_BP Ensemble Neural Network Model for Pump-Turbine Characteristics

为提高抽水蓄能调节系统仿真中水泵水轮机模型精度,提出了一种集成PSO_BP神经网络模型来描述水泵水轮机全特性。首先利用改进Suter变换对水泵水轮机全特性进行处理得到样本数据,然后采用PSO算法优化BP神经网络的初始权值和阈值,反复训练出若干个PSO_BP神经网络,最后将单个PSO_BP网络作为自适应Boosting集成算法的弱学习器,最终构建出水泵水轮机的集成神经网络模型。计算结果表明,与单个BP网络模型相比,该模型具有更好的拟合精度及泛化性能,为进一步研究抽水蓄能调节系统性能奠定了基础。

In order to improve the accuracy of pump-turbine model applied in the pumped storage regulation system simulation ,this paper designs a PSO_BP ensemble neural network model to describe pump-turbine characteristics .Firstly ,the pump-turbine charac‐teristics data are processed by improved Suter-transformation to obtain the sample data .Then ,the PSO algorithm is used to optimize the initial weights and thresholds of BP neural networks and train out several PSO_BP neural networks .Finally ,a single PSO_BP network is treated as a weak learner of adaptive boosting ensemble learning algorithm ,and then the ensemble neural network model for pump-turbine characteristics is completed .The calculation results show that the model has better fitting accuracy and generaliza‐tion performance than a single BP neural network model ,and it provides a good foundation for a further study of the pumped storage regulation system .