SVM-based Implicit Stochastic Scheduling Mode for Cascade Hydropower Stations

摘要：

　　In view of the uncertainty of inflow of cascade reservoirs,an implicit stochastic joint scheduling function model for cascade hydropower stations based on support vector machine(SVM)was established.Taking the cascade hydropower station on the lower reaches of the Yalong River as an example,according to the long series of optimized scheduling simulation operation data,the Gauss radial basis(BRF)kernel function is utilized by LIBSVM for the scheduling function fitting of the cascade reservoirs in the lower reaches of the Yalong River.Besides,combined with particle swarm optimization algorithm,the support vector machine(SVM)model parameters c(penalty coefficient)and g(relaxation coefficient)were optimized.Eventually,the optimized scheduling function model was used for the cascade hydropower station simulation operation.The results show that compared with the existing scheduling technology,the nonlinear SVM scheduling function is better than the linear regression model,and the effect of the nonlinear SVM scheduling function is equivalent to that of the threshold regression model.Therefore,the SVM-based Implicit Stochastic Scheduling method can provide references for the actual operation of the cascade hydropower station.

作者: Wang Qinhui Li Jidong Chen Shijun Wang xiaoyi

作者单位: College of Water Conservancy and Hydropower Engineering,Sichuan Agricultural University,Yaan 625014 College of Water Resources and Hydropower,Sichuan University,Chengdu 610065,China;Business School,Si

会议类型: 国际会议

会议名称: 2018 International Symposium on Water System Operations(ISWRSO 2018)(2018年水资源系统及调度国际研讨会)

会议地点: 北京

会议语种:英文

页码: 1-6

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）