改进遗传算法的无功优化

Reactive Power Optimization Based on Improved Genetic Algorithm

电力系统实现无功优化控制是保证系统电压质量、降低网损的重要措施。对于高维、非线性和连续变量与离散变量共存的电力系统无功优化数学模型，对一般遗传算法的无功优化算法在遗传操作过程中，进行了“灾变”，在选择操作中将轮盘赌和竞标赛方法相结合，对交叉变异算子根据每代个体的实际状况进行自适应调整。通过IEEE30节点算例表明了本文方法可有效提高每代种群的多样性，从而提高了一般遗传算法的无功优化的收敛速度和全局优化特性。

Reactive power optimization is an effective method for improving the electricity quality and reducing the power loss in power system. Reactive power optimization is a higher dimensional, nonlinear and complicated optimizing problem, in which continuous and discrete variables coexist, this article presents ＂cataclysmic GA＂,combining the ＂Roulette Wheel＂ and ＂Tournament＂. Self-adaptive adjusting operators in GA based on the number of iterate and performance of units, speeds up the convergence. The IEEE30 power system, are taken in optimization. The results of the optimization demonstrate that the method presented by this article can improve general GA＇s performance.