基于自适应变异特性粒子群算法的无功优化

Reactive Power Optimization Based on Adaptive Mutation Particle Swarm Optimization Algorithm

针对电力系统无功优化问题,提出了1种自适应变异特性粒子群算法来克服粒子群优化方法容易早熟而陷入局部最优解的缺点。该方法以种群适应度方差为量化指标,动态衡量和监视粒子群体的聚集情况,并对聚集的粒子赋予变异操作,用以提高整个群体的全局寻优能力。通过对IEEE-6和IEEE-30测试系统的无功优化问题计算及结果分析表明该方法快速、高效、准确。

To deal with reactive power optimization problem, an adaptive mutation particle swarm optimization algorithm （AMPSO） is presented to avoid the premature phenomenon in PSO. In AMPSO, swarm fitness variance is applied to judge and monitor particles＇ aggregation condition and mutation operator is acting on aggregating particles to improve method＇s global searching ability. The proposed algorithm is tested on IEEE-6 and IEEE-30 bus systems. The results show its better performance on celerity, accuracy and efficiency.