改进粒子群-禁忌搜索算法在多目标无功优化中的应用

发布时间：2018-06-13 04:56

本文选题：电力系统 + 无功　；参考：《电力自动化设备》2014年08期

【摘要】：针对有功网损、电压偏差和静态电压稳定裕度的多目标无功优化问题,提出一种基于改进粒子群-禁忌搜索算法的多目标电力系统无功优化方法。以最小特征值模为电压稳定裕度指标建立了3个目标函数的单一妥协模型。应用Kent映射产生的混沌序列作为初始种群,保证初始种群的多样性和均匀性。粒子群优化(PSO)算法进行前期计算时,采用凸函数递减惯性权重和自适应学习因子提高算法的收敛速度和精度;针对PSO算法搜索精度不高和陷入局部最优的问题,在PSO算法后期收敛后引入禁忌搜索算法全局寻优。基于群体适应度方差,引入模糊截集理论将模糊集合转化为经典集合,定义了经典集合下的收敛指标,当其值为0时进入禁忌搜索计算阶段,解决2种算法的切换问题。将所提方法应用于IEEE14、IEEE30和IEEE118节点系统中,验证了其有效性和可行性。
[Abstract]:Aiming at the multi-objective reactive power optimization problem of active power loss voltage deviation and static voltage stability margin a multi-objective reactive power optimization method based on improved particle swarm optimization (PSO) and Tabu search algorithm is proposed. A single compromise model of three objective functions is established with the minimum eigenvalue mode as the voltage stability margin index. The chaotic sequence generated by Kent mapping is used as the initial population to ensure the diversity and uniformity of the initial population. Particle Swarm Optimization (PSO) algorithm is used to reduce inertia weight and adaptive learning factor to improve the convergence speed and precision of PSO algorithm, and to solve the problem of low searching precision and falling into local optimum, PSO algorithm can improve the convergence speed and precision of PSO algorithm by using convex function decreasing inertia weight and adaptive learning factor. After the late convergence of PSO algorithm, Tabu search algorithm is introduced for global optimization. Based on the variance of population fitness, fuzzy set theory is introduced to transform fuzzy set into classical set, and the convergence index under classical set is defined. When the value is 0, the convergence index of fuzzy set is defined, and the Tabu search calculation stage is entered when the value is 0, and the switching problem of two algorithms is solved. The proposed method is applied to IEEE 14 IEEE30 and IEEE118 bus systems, and its validity and feasibility are verified.
【作者单位】：四川大学电气信息学院;国网四川省电力公司技能培训中心;国网自贡供电公司;
【基金】：国家自然科学基金重点资助项目(51037003)~~
【分类号】：TM714.3

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