基于改进量子遗传算法的电力系统无功优化

提出一种基于改进量子遗传算法的电力系统无功优化方法。该方法借鉴量子计算的一些概念，采用量子比特对控制变量编码，这种编码方式能表示出许多可能的线性叠加态，从而更好地维持种群的多样性。同时利用搜索到的最佳个体信息更新量子门，加快了该方法的收敛速度，采用群体灾变策略防止该方法陷入“早熟”。分别采用线性规划算法、复合形算法、改进禁忌搜索算法、标准遗传算法、自适应遗传算法和该方法对IEEE 6和IEEE 30节点系统进行无功优化，实验结果表明，该方法全局寻优能力强、收敛速度快。

A Reactive Power Optimization Method Based on Improved Quantum-Inspired Genetic Algorithm

A new improved quantum-inspired genetic algorithm(IQGA)based approach to optimize power system reactive power is proposed,in which several conception in quantum calculation are used for reference and the quantum bit is used in the coding of control variables,such coding mode can represent many possible linear superposition states,thus the population diversity can be maintained better.Meanwhile, the quantum gate is updated by the researched optimal individual information to quicken the convergence speed of this algorithm;besides,the population catastrophe strategy is adopted to prevent this algorithm fall into premature convergence.The reactive power optimization for IEEE 6-bus system and IEEE 30-bus system is performed by linear programming algorithm,Box algorithm,MTSA algorithm, standard genetic algorithm,adaptive genetic algorithm and the proposed algorithm respectively,optimization results show that the proposed algorithm possesses strong global searching capability and it can converge rapidly.