三目标遗传粒子算法的电力系统无功优化

Three-objective Hybrid Algorithm of Genetic and Particle Swarm Optimization for Reactive Power Optimization

电力系统无功优化是提高电网高效运行和节能的关键环节。建立了综合考虑有功网损最小、电压偏差最小及静态电压裕度最大的三目标电力系统无功优化模型。提出了遗传粒子群(GAPSO)混合算法,并将算法运用于IEEE14与IEEE30节点电力系统无功优化中。该算法先通过选择操作,选出优秀的样本,在利用交叉操作增加种群的多样性。然后进行变异操作提高种群的局部搜索能力。通过数据计算和比较GAPSO算法在收敛速度、精度和全局搜索能力上均优于常规GA算法和PSO算法。结果验证了模型和算法的有效性和实用性。

Reactive power optimization of power system is the key point to improve the efficient operation and energy saving of power grid. Based on loss minimization, voltage level best target and maximum static voltage stability margin, this paper established three-objective hybrid algorithm. The algorithm called hybrid algorithm of genetic and particle swarm optimization （GAPSO） algorithm was proposed, and applied to IEEE-14 and IEEE-30 node system for three-objective reactive power optimization. Through the choice of operation, the algorithm selected outstanding sample and increased the diversity of population in the use of the crossover operation to . By testing data and comparing rate of convergence, accuracy and global searching ability of the GAPSO algorithm is found to be superior to conventional GA algorithm and PSO algorithm. The results show the validity of the proposed model and algorithm, which has important theoretical guiding significance for the security and economic operation of power system.