Abstract
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 parametersc(penalty coefficient) andg(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.
Highlights
With the slowdown of hydropower development, the optimal operation of cascade reservoirs has attracted more and more attention
In order to facilitate the comparison analysis concerning the water level prediction results of the linear regression and threshold regression models in literature [3], the mean relative error index was used to represent the degree of fitting with the optimal scheduling trajectory [7], and the Particle Swarm Optimization (PSO)-support vector machine (SVM) prediction yield was compared with linear regression model and the threshold regression model
The absolute difference is the difference between the PSO-SVM model and the linear regression model or the threshold regression model
Summary
With the slowdown of hydropower development, the optimal operation of cascade reservoirs has attracted more and more attention. Major methods for establishing the cascade reservoir function model include multiple linear regression model, threshold regression model and artificial neural network model [3]. Compared with other nonlinear regression methods, the SVM regression algorithm is simple, easy to implement with less complex computation, which enable this algorithm to excel in solving large-scale problems [4]. This investigation intends to solve the problem of implicit stochastic scheduling of cascade reservoirs based on support vector machine technology, so as to provide reference for the actual operation of cascade hydropower stations
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