| The optimal operation of hydropower station group is a highly nonlinear optimization problem with constraints because of the complex hydraulic connection and electric connection between power stations. The solution space of the problem becomes larger and more ruleless when the scale of hydropower station group increases. Because of the curse of dimensionality, traditional algorithms can not obtain a solution which is optimal or satisfactory in the acceptable time, when used to solve the optimal operation of hydropower station group.Particle Swarm Optimization (PSO), as an important branch of intelligent optimization algorithms, has many advantages such as: easy to be realized, converging quickly, independent on the formulation of objective function, no curse of dimensionality, which provides a new way to solve the optimal operation of hydropower station group. Based on the in-depth analysis of PSO's mechanism and performance on searching the optimum, this paper proposes two novel PSOs, Parameter Self-Adjusted Particle Swarm Optimization (PSA-PSO) and Multi-Subgroup Assistant Particle Swarm Optimization (MSA-PSO) to solve the optimal operation of hydropower station group which is a multiple dimension, nonlinear, constrained optimization. PSA-PSO and MSA-PSO are used to simulate the long-term compensation dispatching of hydropower station group on Yangtze River and the short-term scheduling of hydro-thermal system consisting of four cascade hydropower stations and two thermal power stations separately.It is proved by the simulation results that the two novel PSOs reserve many advantages of the basic PSO, and improve the algorithm's performance on exploration and exploitation, which is applicable to solve the multiple dimension, nonlinear, constrained optimization. The two novel PSOs provide feasible methods to solve the optimal operation of large scale hydropower station group and useful experience for application of PSO in science and technology.
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