基于遗传算法和粒子群优化算法的电力系统无功优化

从数学的角度分析,电力系统无功优化是一个多变量、多约束、非连续性的混合非线性规划问题,因此,优化过程十分复杂.以减少有功网损为目标函数建立电力系统无功优化计算的数学模型,基于遗传算法和粒子群优化算法,提出一种新颖的混合策略来求解无功优化问题.IEEE 6和IEEE 14节点系统的仿真计算结果表明:与单一的遗传算法或粒子群优化算法相比,该混合策略在优化效果方面具有明显的优势.

Reactive power optimization of power system based on genetic algorithm and particle swarm optimization algorithm

From the view of mathematics,reactive power optimization problems are large-scale nonlinear non-continuous optimization problems with a large number of variables,constraints,and uncertain parameters,so the optimization becomes very complex.Reducing active power loss on grid was considered as the main object function to establish reactive power optimization mathematic model.By integrating genetic algorithm(GA) with particle swarm optimization algorithm(PSO),a hybrid strategy for the optimal reactive power flow(ORPF) problem was proposed.Numerical simulations on the IEEE 6 and IEEE 14 test systems illustrated that the proposed hybrid strategy is more effective than either of the algorithnis mentioned above.