【作者】 姜生斌；

【导师】 纪昌明； 李继清；

【作者基本信息】 华北电力大学（北京） ， 水文学及水资源， 2008， 硕士

【摘要】 针对当前梯级水电站水库优化调度方法中普遍存在的“维数灾”问题,本文提出一类基于免疫微粒群算法(I-PSO)的梯级水电站水库优化方法。微粒群算法(PSO)是一类随机全局优化技术,其流程简单易实现、算法依赖的参数少。I-PSO是将免疫信息处理机制和标准PSO有机结合在一起的一种改进的微粒群算法,一方面采用微粒浓度选择机制来保持微粒多样性,较好的解决了算法可能出现的局部收敛问题;另一方面通过在算法中加入疫苗接种等操作来指导寻优过程,加快了算法后期的收敛速度。经实例验证,运用I-PSO调度模型在梯级水电站水库的长期和短期调度中取得良好的优化效果,能够为解决复杂的梯级水电站优化问题提供一条有效、可行的新途径。更多还原

【Abstract】 Since the problem of“dimension disaster”exits widely in current optimal dispatching methods of cascade hydropower station, this paper has presented a cascade hydropower station optimal method based on Immune Particle Swarm Optimization (I-PSO) Algorithm. Particle Swarm Optimization (PSO) is a stochastic global optimization algorithm, which is simple to implement and depends on fewer parameters. I-PSO is an improved PSO algorithm which combined immune information-processing mechanism and standard PSO, on the one hand the local convergence issues that may arise can be solved well by concentration of particulate selection mechanism used to maintain the diversity of particles; on the other hand, the algorithm convergent speeds up latter through adding vaccination to guide the optimization process. As the case study shows, I-PSO dispatching model obtained well optimal results by applying I-PSO dispatching model in cascade hydropower station, which provided an effective and feasible way to solve complex cascade optimization problem.更多还原