Optimization of Reservoir Flood Control Operation Based on Multialgorithm Deep Learning

Abstract

With the rapid development of China’s social economy, it is the most important task for the water conservancy industry to make use of the existing water conservancy engineering measures to carry out the research on river basin flood control dispatching. Large-scale joint operation of river basins usually needs to consider meteorological and hydrological conditions, historical flood data, multireservoir engineering conditions, and multiple flood control targets, which is a complex decision-making problem. Therefore, electing the optimal operation model of reservoir flood control optimization is very important. In this paper, Luanhe River Basin is taken as the research area, and three kinds of constraints, namely, water balance constraint, reservoir flood control capacity constraint, and water release decision constraint, are set to construct the flood control optimization model. Taking the minimum square of the sum of reservoir discharge and interval flood discharge as the objective function, genetic algorithm (GA), particle swarm optimization (PSO), Spider swarm optimization (SSO), and grey wolf optimization (GWO) are introduced into flood control optimal operation to seek the minimum value of objective function, and the results are compared and analyzed. Through the analysis of optimization results, the optimization ability and convergence effect of grey wolf optimization algorithm are better than those of genetic algorithm and particle algorithm, and the results are more stable than those of spider swarm algorithm. It has a good model structure and can make full use of the results of three wolf groups for optimization. Through the analysis of scheduling results, the results of genetic algorithm and particle swarm optimization algorithm are similar, while those of spider swarm optimization algorithm and grey wolf optimization algorithm are similar and slightly better than those of the first two. Moreover, the search range of grey wolf optimization algorithm for solving long sequence problems is wider and the calculation time is shorter. Therefore, the grey wolf optimization algorithm can be applied to solve the flood control operation optimization model of Panjiakou Reservoir Group.

Application of HSMAAOA Algorithm in Flood Control Optimal Operation of Reservoir Groups

“…However, with the increase in the operation period and the number of reservoirs, the problems of slow convergence and "dimension disaster" will appear [14]. With the development of intelligent optimization algorithms, the genetic algorithm [15][16][17][18][19], particle swarm optimization algorithm [18,[20][21][22], immune algorithm [23], firefly algorithm [24], and their improved algorithms are also used in reservoir flood control operation. The intelligent optimization algorithm has solved the "dimension disaster" problem of reservoir flood control operation, but the randomness is too strong, and it is easy to fall into the local optimal solution, and the solution result is not stable.…”