多目标电网无功优化的量子遗传算法

Multi-Objective Reactive Power Optimization Using Quantum Genetic Algorithm

在传统无功优化模型中引入静态电压稳定指标,建立了以网损最小、静态电压稳定裕度最大为目标的多目标无功优化模型。还介绍了量子遗传算法,它是一种基于量子计算概念的智能算法,用量子比特为基本信息位编码染色体,用基于量子概率门的量子变异实现个体进化,其收敛速度和全局寻优能力优于传统进化算法。将该算法用于电力系统无功优化并仿真计算了IEEE14、30节点系统,结果验证了模型和算法的有效性。

This paper incorporates the voltage stability view to reactive power dispatch and control problem. A model of multi-objective reactive power optimization is established, which takes into account of loss minimization, voltage stability margin maximization and high service quality. Quantum genetic algorithm（ QGA） is a novel algorithm called probability evolutionary algorithm. It uses quantum bit as coding which is different from conventional binary coding completely. QGA makes full use of superposition of quantum states to keep diversity of population and avoid prematurity of population. An implementation for QGA had been applied to reactive power optimization in this paper. The simulations are carried out on IEEE 14,30 bus system, and the results show the validity of the proposed model and algorithm.