基于IBBDE算法的交直流混联系统无功优化

随着高压直流输电工程(HVDC)投产规模持续增长,交直流混联电网的格局初步形成,给传统电网无功优化带来挑战。文中提出一种改进骨干差分进化算法(Improved Bare-bones Differential Evolution,IBBDE)求解交直流混联系统无功优化问题。在骨干差分进化算法的基础上,IBBDE算法采用广义反向学习初始化种群和自适应调整交叉概率的改进措施以提升种群的全局寻优能力。以含HVDC的IEEE 30节点系统为算例进行分析,结果表明,与差分进化算法和骨干差分进化算法相比,所提IBBDE算法可获得更优的无功优化效果,且寻优稳定性更好。

An Improved Bare-bones Differential Evolution Algorithm for Reactive Power Optimization of Hybrid AC-DC System

With the increasing growth of HVDC transmission projects,preliminary formation of AC-DC hybrid power grid brings new challenges to reactive power optimization of traditional power grid.Aiming at this problem,an improved bare-bones differential evolution algorithm(IBBDE)is proposed to solve the reactive power optimization of hybrid AC-DC system.Initialization of population using generalized opposition based learning and self-adaption of crossover factor are introduced to enhance the global convergence of algorithm.The IBBDE algorithm is tested on IEEE 30-bus system with HVDC,the simulation results show that,comparing with DE and BBDE algorithm,the proposed IBBDE algorithm can achieve better reactive power optimization effect and has superiority on optimization stability.