基于免疫粒子群算法的电力系统无功优化

为提高粒子群优化(particle swarm optimization,PSO)算法的收敛性能,将免疫算法(immunity algorithms,IA)的免疫信息处理机制引入到标准粒子群算法,形成一种新的优化算法,即免疫粒子群算法。该算法将免疫算法的免疫记忆和自我调节机制引入PSO,并采用基于粒子浓度机制的多样性保持策略;同时,用免疫算法的"接种疫苗"和"免疫选择"来指导搜索过程。改进后的算法可以很好的保持优化过程中粒子群的多样性,抑制优化过程中出现的退化现象,保证算法的收敛精度和收敛速度。IEEE 30节点系统算例仿真表明,IA-PSO算法与标准PSO算法相比,能够及时跳出局部最优得到全局最优解,且收敛速度快、精度高。

Reactive Power Optimization Based on Particle Swarm Optimization Algorithm with Immunity

To improve the convergence performance of particle swarm optimization(PSO) algorithm,the immunological memory and self-regulation mechanism are led into standard PSO to form particle swarm optimization algorithm with immunity. In the proposed algorithm the diversity holding strategy based on particle concentration mechanism is adopted,meanwhile the vaccination and immunoselection in immune algorithm are used to guide the search process. The improved PSO can hold the diversity of swarm during the optimization process,and eliminate the degeneration phenomenon occurring in the optimization process,thus both convergence accuracy and convergence speed of the improved algorithm can be ensured. The simulation of IEEE 30-bus system that is taken as case study is simulated and the simulation results show that comparing with standard PSO algorithm,the proposed PSO algorithm with immunity possesses following advantages such as high convergence speed,accurate and the ability of getting rid of local optima in time to achieve global optimal solution,etc.