基于改进免疫遗传算法的无功优化

针对目前电力系统无功优化算法所存在的问题提出了一种改进的免疫遗传算法，该算法把模糊逻辑、模拟退火和免疫算法相结合，根据模糊逻辑获得变化的交叉和变异算子，采用退火免疫方法对抗体进行选择，用免疫算子进行个体更新，从而增加了群的多样性，避免陷入局部最优。同时，还采用十进制整数编码和保存最优个体法来提高计算速度和精度。最后以IEEE 30-bus系统为例对算法的性能和求解精度进行了测试，结果表明本文提出的算法比其他遗传算法在计算速度和全局收敛方面有了很大提高。

Reactive Power Optimization Based on Improved Immunity Genetic Algorithm

To remedy defects of existing reactive power optimization algorithm, an improved immune genetic algorithm, which integrates fuzzy logic and simulated annealing algorithm with immune algorithm, is proposed. To avoid falling in with local optima, According to fuzzy logic, variable crossover and mutation operators are obtained; by use of annealing immune algorithm the antibody is selected; the immune operator is used to update the individuals, thus the diversity of group is reinforced and the local optima can be avoided. Besides, the calculation accuracy and its speed are improved by decimal integer encoding and reserving optimal individuals. The performance of the proposed algorithm and the accuracy of its solution are tested by IEEE 30-bus test system, simulation results show that the proposed algorithm is far better than existing genetic algorithm for reactive power optimization in global convergence and calculation speed.