基于混沌量子免疫算法的电力系统无功优化

Power System Reactive Power Optimization Based on Chaos Quantum Immune Algorithm

针对电力系统无功优化问题的离散性和多约束非线性特点,提出一种混沌量子免疫算法的电力系统无功优化方法。该算法利用量子比特和由Logistic映射产生的混沌变量来初始化寻优量子抗体,从而保证了算法的遍历寻优和搜索高效性。通过量子旋转门来实施抗体的克隆扩增和变异,利用线性变换将量子抗体由单位空间映射到寻优问题的解空间。IEEE30节点仿真验证混沌量子免疫算法用于电力系统无功优化的可行性和有效性。

In view of the discreteness and multiple-constraint nonlinearity in the reactive power optimization for the power system, this paper proposes a reactive power optimization method based on Chaos Quantum Immune Algorithm（CQIA）. The algorithm uses quantum bits and chaotic variables produced by Logistic mapping to initialize optimization of quantum antibodies to ensure the traversal optimization and searching efficiency of the algorithm. Antibody clonal amplification and mutation are implemented by the quantum revolving door and quantum antibody is reflected from the unit space to the solution space of optimization by using the linear transformation. Simulation results of the IEEE30-bus system show that the proposed algorithm is feasible and effective.