Abstract: Reactive power optimization of the power system is an effective way to keep the power system safe and economical. The main purpose of reactive power optimization is that it can improve the voltage quality and reduce the active power loss of the power system. The reactive power optimization problem of the power system is a complex and nonlinear problem and it should adjust the variables including control variables and state variables. This paper establishes a differential evolution flower pollination algorithm based on particle swarm optimization (DFPA-PSO) in view of the shortcomings of the accuracy of the traditional particle swarm optimization algorithm. The DFPA-PSO combines global search, local search and mutation operations with the flower pollination algorithm. Not only can it widen the search space of particles, but also it increases the diversity of the particles. The DFPA-PSO is applied to the IEEE-14 bus system, which takes into account of loss minimization, voltage level best target and maximum static voltage stability margin. Compared with other algorithms, the results show that DFPA-PSO has stronger global searching ability, faster convergence rate, better robustness and the active power loss is also reduced, thus proving the superiority of DFPA-PSO.

Key words: Flower pollination algorithm, three-objective optimization, reactive power optimization

摘要： 电力系统无功优化，是确保电力系统安全、经济运行的一个有效处理手段，其主要目的是为了改善电压质量和减少电力线路的有功网损。电力系统无功优化问题包含对控制变量和状态变量的调节，是一个复杂的、非线性混合整数规划问题。针对于传统粒子群算法收敛速度和收敛精度方面的缺陷与不足，文章提出基于粒子群的差分变异花朵授粉算法(DFPA-PSO)。该算法融入花朵授粉算法中的全局搜索和局部搜索过程和差分算法中的变异操作。拓宽粒子搜索区域的同时，还增加粒子的多样性。将该算法应用于IEEE-14节点的标准测试电力系统中，综合考虑有功网损、电压偏移和电压稳定裕度三目标优化模型，将实验结果与其他算法进行相比较，明显看出该算法的寻优能力强，收敛速度优于其他算法，有功网损也有所降低，鲁棒性好，从而证明了本算法的优越性。

关键词: 花朵授粉算法, 三目标优化, 无功优化