三角骨架差分进化算法的电力系统无功优化

Triangle Differential Skeleton Evolution Algorithm for Reactive Power Optimization

在无功优化中通常是以减少线路中的有功网损、降低电网无功补偿容量、提高电能质量等方面为目标进行优化。建立了以减少有功网损,降低电压偏移以及提高电压稳定裕度的三目标优化模型。在传统的差分进化算法(Differential Evolution Algorithm)中,控制参数和差分变异策略在对待优化解的问题较为敏感。为克服这一缺陷进一步提出的一种具有自适应参数的的差分进化算法。首次引入全新的三角高斯变异方式,在样本中随机选出的三个不同的值取均值μ,标准差取任意两差的绝对值的平均值为标准差δ进行高斯分布。将其运用于电力系统IEEE-14节点的系统中进行仿真,将传统差分算法和粒子群算法与本算法进行比较,验证本算法的优越性与实用性。

In reactive power optimization process,the objective is to reduce the active power loss,and reactive power compensation capacity,and improve the power quality. A three-objective-optimization model is established to reduce the active power loss,and voltage deviation,and to improve the voltage stability margin. In traditional differential evolution algorithms,the control parameters and the differential mutation strategy are more sensitive to the problem of the optimal solution. A differential evolution algorithm with adaptive parameters is proposed to overcome this limitation. A new method of triangular Gauss variation is introduced,and the mean value of the three different values of μ is then selected,and the mean value of the absolute value of the standard deviation is δ for Gauss distribution. The simulation is carried out on the system of the IEEE-14 node of power system. The traditional difference algorithm and particle swarm optimization algorithm are compared with the presented algorithm to verify the superiority and practicability of the algorithm.