多目标差分进化算法的电力系统无功优化

Multi-Objective Differential Evolution Algorithm For Reactive Power Optimization

在传统电力系统无功优化(Reactive Power Optimization,RPO)模型中引入电压水平指标,建立了以网损最小,电压水平最好为目标的多目标差分进化算法(Differential Evolution Algorithm)的模型。针对基本差分进化算法易陷入局部最优解、收敛速度慢的缺点,提出一种具有自适应参数策略的改进差分进化算法并首次用于多目标电力系统无功优化问题。通过在算法进化过程中调整变异因子F和交叉因子CR,在初期增加种群的多样性、扩大全局搜索区域;从而可以避免算法陷入局部最优解;同时在后期也加快了收敛速度。将该算法用于电力系统无功优化并仿真计算了IEEE-14节点标准测试系统,结果验证模型和算法的有效性。

The voltage level to reactive power optimization dispatch and control problem is incorporated. A model of reactive power opti- mization is established based on multi-objective differential evolution, which takes into account of loss minimization, voltage level best target. Considering the drawbacks of traditional differential evolution （DE） algorithm such as premature and slow search speed, a strategy of self-adapting parameter improved differential evolution algorithm was proposed and first applied in reactive power optimization problem. By adjusting the mutation F and crossover CR during the evolution process, the diversity of population is increased and the global search area is expanded, which avoids algorithm into a local optimal solution, at the same time, the convergence speed is accel- erated later. The simulations are carried out on IEEE-14 bus system, and the results show the validity of the proposed algorithm.