基于改进粒子群算法的电力系统无功优化

电力系统无功优化问题是一个多变量、多约束的混合非线性规划问题。提出了一种改进粒子群算法用以解决这一复杂优化问题。在改进的算法中,首先结合混沌优化思想对粒子群进行初始化,减轻了粒子初始位置的选择对算法优化性能的影响;在进化过程中引入了自探索行为,使得粒子的搜索过程更加符合实际;引入了变异机制及3种判断陷入局部最优的标准,当发现粒子群陷入局部最优时,通过变异,帮助粒子跳出局部陷阱,增加发现最优解的机会。给出了问题的求解方法,并对IEEE 6、14节点系统进行了仿真计算,实验数值对比表明了算法的可行性和有效性。

Reactive power optimization of power system based on modified particle swarm optimization algorithm

Reactive power optimization(RPO) is a multi-variable and multi-constraint nonlinear optimization problem.A modified particle swarm optimization(MPSO) algorithm was provided to solve it.In the MPSO the particles were initialized with chaos optimization method in its sub-area,which reduced the influence caused by the particle's initial position,and then the tentative behavior was introduced to particles,which made it more practical.Finally,mutation mechanism was introduced and three criterions had been given to judge whether the population was trapped into local optimum,when it happened.MPSO can help particles to escapes from local optimum,thus the particles has more great chances to find the global optimum.The method adopting MPSO algorithm was given to solve the reactive power optimization(RPO) problem,and was applied in IEEE 6,14 bus system.Numerical results show its validity and effectiveness.