基于改进遗传算法与原对偶内点法的无功优化混合算法

基于改进遗传算法和原对偶内点法提出一种求解无功优化问题的混合算法。首先通过改进遗传算法求解无功优化问题中的离散变量，然后采用原对偶内点法求解与已获得离散变量最匹配的连续变量。在改进遗传算法中采用交叉、变异算子并基于可行域规则处理离散约束，有效提高了混合优化算法的整体寻优效率。在IEEE 118节点系统中的仿真计算结果验证了本文方法的有效性。该方法已应用于福建电网自动电压控制系统中。

A Hybrid Reactive Power Optimization Algorithm Based on Improved Genetic Algorithm and Primal-dual Interior Point Algorithm

Based on improved genetic algorithm (IGA) and primal-dual interior point (PDIP) algorithm,a hybrid reactive power optimization algorithm is proposed. In this algorithm,at first the reactive power optimization problem is converted to integer nonlinear programming problem with discrete control variables which are solved by IGA; then by use of PDIP algorithm,the continuous variables which can match with the obtained most are solved,and the reactive power optimization is changed into a nonlinear programming problem with continuous control variables. In IGA,using crossover operator and mutation operator and based on the rule of feasible region the discrete constraints are processed,thus the global optimization efficiency of the hybrid optimization algorithm is raised. Numerical simulation results on the IEEE 118 test system show that the proposed method is effective. The proposed method has been applied in AGC system of Fujian power grid.