改进粒子群优化算法在水电站群优化调度中的应用研究

为克服常规粒子群优化算法易早熟、后期收敛慢且易陷入局部最优解的缺点，本文提出一种新的惯性权重系数更新策略——自适应指数惯性权重系数(SEIWC)代替线性递减惯性权重系数(LDIWC)，同时，将遗传算法中的染色体交叉、变异思想引入粒子的更新策略，提高粒子的多样性，增强算法的全局搜索能力。使用Rosenbrock函数和Schaffer函数验证了改进粒子群优化算法的有效性。以福建电网闽江流域水电站群优化调度为例，建立基于改进粒子群优化算法的库群长期优化调度模型。计算结果表明，该模型的调度结果显著优于常规粒子群优化算法，与逐步优化算法结果水平相当。

Application of improved particle swarm optimization algorithm to operation of hydropower station group

A new strategy of inertia weight coefficient for particle swarm optimization (PSO) algorithm, namely self adaptive exponential inertia weight coefficient(SEIWC), is proposed to solve the problems of prematurity, slow convergence and falling into local minimum, which may happen to the application of conventional particle swarm optimization algorithm using linearly increasing inertia weight coefficient. The concepts of crossover and mutation of chromosome are introduced to improve the global searching ability of PSO algorithm. The effectiveness of the proposed algorithm is verified using the Rosenbrock function and Schaffer function. The improved algorithm is applied to formulate the optimal operation of hydropower station group in the Minjiang River, Fujian Power Grid, with maximum energy regarded as the objective function. The result indicates that the improved algorithm is much better than the traditional algorithm and is comparable with that of progressive optimization algorithm.