基于混合粒子群算法的梯级泵站优化调度

针对大型梯级泵站工况复杂多变、安全性要求高的运行特点,基于流量平衡的理论,建立梯级泵站运行费用最小的优化目标,同时把系统一次运行周期内泵站机组的启停次数作为衡量维护费用的指标,建立泵站启停次数最小的优化模型,运用线性加权法将两个优化目标组合成一个泵站系统综合运行费用最少的优化调度模型,最终运用动态规划法和粒子群算法进行研究分析,并尝试采用免疫思想通过克隆免疫算子和疫苗接种算子优化粒子群算法,达到提高搜索范围和精度的目的。将其应用于山西某梯级泵站工程实例,仿真研究分析表明免疫粒子群算法(IAPSO)在优化泵站系统综合运行费用上更加节省成本而且提高了搜索精度和收敛速度。

Optimal Schedul of Cascade Pumping Stations Based on Hybrid Particle Swarm Optimization Algorithm

Based on the theory of the flow balance,the minimum operating cost of the cascade pumping stations is set up based on the theory of flow balance.At the same time,the number of start and stop times of the pump station unit are taken as a mea sure of maintenance cost,and the minimum number of stop and stop times of the pumping station are established.The optimization model is combined with the linear weighting method to combine the two optimization targets into an optimal scheduling model with the least cost of the pump station system.Finally,the dynamic programming method and particle swarm optimization algorithm are used to study and analyze,and the immune idea is tried to optimize the particle swarm optimization by cloning immune operator and vaccination operator.To improve the scope and precision of the search.It is applied to an example of a cascade pumping station in Shanxi.The simulation analysis shows that the immune particle swarm optimization(IAPSO)can save cost and improve the search precision and convergence speed in optimizing the comprehensive operation cost of the pumping station system.