基于遗传算法和内点法的无功优化混合策略

基于遗传算法与内点法，文中提出了一种新颖的混合策略来求解无功优化问题：不考虑无功优化中的离散约束，采用内点法求解得到初始解；根据优化变量的不同性质，将原无功优化问题分解为离散优化和连续优化2个子问题，并采用遗传算法和内点法交替求解。在遗传迭代的不同阶段，针对种群个体的不同特点，分别对遗传算法和内点法的具体实施方案进行了动态调整，使两者的优化结果互为基础、相互利用，保证了混合策略的整体寻优效率。IEEE30和IEEE118节点系统的仿真计算结果表明：与其他混合算法相比，该混合策略在计算速度和优化效果方面都具有明显的优势。

A HYBRID STRATEGY BASED ON GA AND IPM FOR OPTIMAL REACTIVE POWER FLOW

By integrating a genetic algorithm (GA) with an interior point method (IPM), a hybrid strategy for the optimal reactive power flow (ORPF) problem is proposed in this paper. First, the original ORPF problem is converted to a continuous non-linear programming problem by relaxing the discrete variables, and the initial solution is obtained by the IPM. Then according to the discrete feature of the control variables, the original ORPF problem is decomposed into a continuous optimization sub-problem and a discrete optimization sub-problem, which are solved by IPM and GA respectively. By solving the two sub-problems alternately, the optimal solution of the ORPF problem can be obtained. In different phases, the implementing of IPM and GA are dynamically adjusted according to the characteristic of individuals. IPM and GA take advantage of each other, so that the efficiency of the hybrid strategy is greatly improved. Numerical simulations on the IEEE 30 and the IEEE 118 test systems illustrate that the proposed hybrid strategy is effective.