基于改进布谷鸟算法的电力系统最优潮流计算

针对电力系统最优潮流问题,提出一种融入量子计算和混沌局部搜索策略的改进布谷鸟算法(QCCS),即对布谷鸟算法的个体进行量子位编码,通过叠加态的量子位实现多样化种群,并在算法每次迭代的优化值附近进行混沌局部搜索进而增加布谷鸟算法的局部搜索能力,同时采用量子门变换使每个个体朝最优个体进化,从而提高算法的寻优能力。最后以IEEE 118节点系统的最优潮流计算问题为例,应用QCCS进行仿真计算。通过与其他方法(PSO、GA、CS)计算结果进行对比分析,验证了QCCS算法求解电力系统最优潮流问题的有效性,从而为电力系统最优潮流(OPF)问题的求解提供了一种新方法。

An Improved Cuckoo Search Algorithm for Optimal Power Flow Problem

For solving optimal power flow problem in power systems, we propose a new cuckoo search algorithm with the quantum computation and chaotic local search strategy (QCCS) to increase the diversity of population and local search capability of the basic cuckoo search algorithm. The proposed algorithm encodes the individual with quantum bits to achieve the purpose of the diversity of population through the superposition state of quantum bits, and increase the local search ability by using a chaotic local search operator in the vicinity of optimal value for each iteration. The quantum gate was used to update each individual towards the best individual for improving the algorithm''s searching ability. The proposed QCCS algorithm is applied to the IEEE 118-node system. Compared with PSO, GA and CS, the results verify the effectiveness of the method. Thus, it provides a new method for solving optimal power flow problem.