应用改进粒子群算法求解松江河梯级水电站短期优化调度问题，建立梯级电站发电量最大和发电效益最大短期优化调度数学模型。针对粒子群算法存在的后期收敛速度慢和易陷入局部最优等缺点，引入收缩因子和基于遗传思想的变异算子对其进行改进。应用改进粒子群算法对松江河梯级水电站进行短期优化调度，分别采用发电量最大和发电效益最大数学模型进行算例分析。结果表明：对梯级电站进行短期优化调度可以提高梯级电站的整体质量和效益；应用改进粒子群算法求解梯级电站短期优化调度问题在求解时间、精度上都取得了满意的效果。

Improved Particle Swarm Optimization (PSO) is applied to solving the short-term optimal scheduling of Songjianghe cascade hydropower stations. Two mathematic models characterized respectively by maximum  power generation and maximum  generation profit are established. Shrinkage factor and hereditary mutation operator are adopted to overcome the shortcomings of standard PSO like precocity and slow convergence in late stage. These  mathematical models are employed in the optimal scheduling analysis for Songjianghe cascade power stations, and the results show that the quality and benefit of the stations could be improved by short-term optimal scheduling. Both the calculation time and accuracy by applying the improved PSO are satisfactory.