基于混沌免疫混合算法的多目标无功优化

针对目前评价多目标函数解的不足，提出了将多目标函数各个解映射成多维空间中不同的点，利用这些点与理想点之间的欧氏距离来衡量各个解的优劣；同时针对无功优化、混沌优化算法和免疫算法的特点，提出了在采用免疫算法进行无功优化的记忆抗体群中，运用混沌优化方法和免疫算法的交叉和变异等操作对无功优化的连续变量和离散变量进行交替优化求解，并将它们运用于以降低有功损耗，提高电压稳定裕度及减小电压偏移为目标的无功优化中；通过 IEEE-30和IEEE-118节点算例系统验证了混合算法及最优解评价方法的正确性和可行性。

A Hybrid Algorithm Based on Chaos Optimization Algorithm and Immune Algorithm for Multi-Objective Optimal Reactive Power Flow

In order to avoid the defect in the traditional evaluation of multi-objective solutions, a new hybrid algorithm to evaluate multi-objective function solutions is proposed. In the proposed algorithm, the solutions of multi-objective function are mapped as different points in multi-dimension space and by use of Euclidean distances between these points and ideal point it is judged that which one of these solutions are superior as well as which ones are inferior; meanwhile according to the features of optimal reactive power flow (ORPF), chaos optimization algorithm (COA) and immune algorithm (IA), it is put forward that in the remember antibody group in which the IA is used to optimize the reactive power flow, the continuous variables and discrete variables are alternatively optimized and solved by means of COA as well as interlace operation and mutation operation of IA, and applying these solutions to reactive power optimization aim at reducing active power loss, improving voltage stability margin and decreasing voltage deviation. The correctness and feasibility of the proposed hybrid algorithm and the optimal solution evaluation method are verified by IEEE 30-bus system and IEEE 118-bus system.