基于改进微分进化算法的电力系统无功优化

Reactive Power Optimization in Power System based on Differential Evolution Algorithm

针对配电网网架规划问题,在基本微分进化算法基础上,引入改进机制,提出一种基于改进微分进化算法的电力系统无功优化算法。新算法通过参考粒子群算法惯性权重思想,引入惯性加权系数,在计算初期能够维持个体的多样性,后期能够加快算法的收敛速度,提高了微分进化算法的性能。将该算法应用于电力系统无功优化中,仿真结果表明:使用该算法优化的网损平均值更低,寻优性能更好,优化的网损值集中在较小的区间。

To address the problem of the distributinon network optimal planning, a Modified Differential Evolution （MDE） algortihm is proposed for realizing linear system approximation. The modified algorithm introduces an inertia scaling factor, which can dynamically maintain the diversity of the individuals at early stages and quicken convergence speed of the algorithm at later stages. Thus the performance of DE algotihm is improved. The algorithm is used to the reactive power optimization in power system. Simulation results show that the average transmission loss is lower, with better optimization performance and smaller area of optimized transmission loss values.