摘要
针对电力系统限流措施优化问题不连续、非线性的特点,提出一种并行免疫粒子群算法。该算法将免疫算法的自我调节机制引入粒子群算法,采用基于粒子相似度的选择机制,保证优化过程中粒子的多样性。根据粒子编码的特点引入疫苗接种概念,有效减少了粒子最优片段丢失的概率,保证算法的收敛精度和收敛速度。并在Matlab并行计算平台上实现免疫粒子群算法的并行化。算例表明该算法具有较强的全局优化能力和收敛稳定性,且计算时间短,有较强的实用意义。
According to the discontinuous and nonlinear characteristics of the optimization of short circuit current limiting strategies for power system, a parallel immune particle swarm optimization algorithm is proposed. The algorithm combines self-regulation mechanism of immune algorithm with particle swarm optimization algorithm, and adopts the selection mechanism based on particle similarity to ensure the diversity of particles in the optimization process. The introduction of vaccination concept according to the characteristics of particle code effectively eliminates the chance of missing the best pieces of particles, thus both convergence accuracy and convergence rate can be ensured. This paper achieves parallelism of immune particle swarm optimization algorithms on Matlab parallel computing platform. The results show that the proposed algorithm has strong global optimization ability and convergence stability, and optimization time is short, so it is practicable.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2012年第8期15-19,56,共6页
Power System Protection and Control
关键词
电力系统
限流措施
优化
免疫算法
粒子群算法
免疫粒子群算法
并行计算
power system
short circuit current limiting strategy
optimization
immune algorithm
particle swarm optimizationalgorithm
immune particle swarm optimization algorithm
parallel computing
