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为进一步提高梯级水电站水库优化效果,提高水电站年发电量,本文作者以梯级电站为例,研究了蝙蝠算法的混合改进;混合了粒子群算法,以及加入小生境排挤技术;并将改进的蝙蝠算法应用于梯级水电站中长期优化调度。通过以粒子群算法、蝙蝠算法和提出的改进算法对某梯级电站进行优化计算,结果显示,改进的算法优于粒子群算法和蝙蝠算法,发电量高于其他两种算法,弃水更少,并证明本文提出的混合蝙蝠算法在求解具有复杂约束条件的非线性的梯级水库优化调度问题时,具有求解结果更优、收敛速度快的优点,为解决梯级水电站中长期优化调度问题提供了一种新的有效方法。
In order to further improve the optimization effect of cascaded hydropower reservoirs and improve the annual power generation of the hydropower station, the author takes the cascaded hydropower station as an example to study the hybrid improvement of the bat algorithm, the hybrid PSO algorithm and the niche exclusion technique. The improved bat algorithm Applied to cascade hydropower stations in the long-term optimal scheduling. By using particle swarm algorithm, bat algorithm and proposed improved algorithm to optimize a cascade hydropower station, the results show that the improved algorithm is superior to particle swarm optimization algorithm and bat algorithm, the power generation is higher than the other two algorithms, And proves that the hybrid bat algorithm proposed in this paper has the merits of better solution and faster convergence in solving nonlinear scheduling problems of cascade reservoirs with complex constraints, and provides a solution to the long-term optimal scheduling problem of cascaded hydropower stations New and effective method.